What is difference between scikit-learn's MLPClassifier vs Keras Sequential Model?

MLPClassifier stands for Multi-Layer Perceptron Classifier, part of Scikit-learn's neural_network module.

It’s a high-level abstraction for a feedforward neural network that:

Trains using backpropagation

Supports multiple hidden layers

Uses common activation functions like 'relu', 'tanh'

Is optimized using solvers like 'adam' or 'sgd'

Is focused on classification problems

from sklearn.neural_network import MLPClassifier

clf = MLPClassifier(

    hidden_layer_sizes=(64, 32),  # Two hidden layers: 64 and 32 neurons

    activation='relu',            # Activation function

    solver='adam',                # Optimizer

    max_iter=300,                 # Max training iterations

    random_state=42

)

clf.fit(X_train, y_train)

What is Sequential (Keras) Model?

The model you showed uses Keras (TensorFlow backend) and gives you lower-level control over:

Architecture design (layers, units, activations)

Optimizer details

Training loop customization

Loss functions and metrics

Fine-tuning and regularization options

Below is Kera's example 

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

model = Sequential([

    Dense(64, activation='relu', input_shape=(input_dim,)),

    Dense(32, activation='relu'),

    Dense(1, activation='sigmoid')

])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

model.fit(X_train, y_train, epochs=10, batch_size=32)

Below are the key differences between MLPClassifier and Sequential 

Feature MLPClassifier (Scikit-learn) Sequential Model (Keras)

Level of Control High-level abstraction Low-level, full control

Custom Layers/Design Limited (Dense-only) Highly flexible (any architecture)

Use Case Quick prototyping/classification Production-ready, deep customization

Loss Functions Handled internally You explicitly choose (binary_crossentropy, etc.)

Training Control .fit(X, y) only Full control over training loop

Model Evaluation score, predict_proba, etc. evaluate, predict, etc.

Built-in Regularization Basic (L2, dropout via early stopping) Advanced (dropout, batch norm, callbacks, etc.)

Performance Tuning Less flexible Very flexible (custom metrics, callbacks, etc.)

When to use what? 

Scenario Use MLPClassifier Use Keras Sequential Model

Simple classification task ✅ Quick and effective ❌ Overkill

Need advanced model architecture ❌ Limited ✅ Full control

Custom training process, callbacks, tuning ❌ ✅

Interoperability with other scikit-learn tools (e.g., pipelines) ✅ ❌

You want to deploy a deep learning model ❌ ✅

In summary, 

Use MLPClassifier for quick experiments and classic machine learning pipelines.

Use Keras Sequential API when:

You want deep learning capabilities

You need fine-tuned control

You're building complex architectures

Let me know if you'd like a side-by-side example for the same dataset using both!

What are typical threshold for imputation?

When deciding whether to impute missing data, the proportion of missing values is an important factor.

General Rule of Thumb for Imputation Thresholds:

Missing % of a Column Recommended Action

< 5% Impute (mean, median, mode, etc.) or drop rows if impact is negligible

5–30% Consider imputation; carefully analyze patterns and impact

> 30% Consider dropping the column or using advanced methods (e.g., model-based imputation)

If the example dataset is having 20K rows and the missing values are oney 18, that's a 0.09 percent missing values. Since it falls in the < 5% range, 

Mean Imputation

Definition:

Replace missing values in a column with the average (mean) of the non-missing values.

Formula:

Mean = ∑𝑥𝑖 / 𝑛 

​When to Use:

The data is normally distributed (i.e., symmetric).

No significant outliers present.

Pros:

Simple and fast.

Preserves the overall mean of the data.

Cons:

Sensitive to outliers — large or small extreme values can skew the mean.

Can reduce variability in the data (makes imputed values common).

Example:

Data: [10, 12, 13, 11, NA]

Mean = 11.5 → impute missing value with 11.5

2. Median Imputation

Definition:

Replace missing values with the median (middle value) of the non-missing data.

When to Use:

The data is skewed (not symmetric).

Outliers are present — median is more robust to them.

Pros:

Not affected by outliers.

Maintains the central tendency better in skewed distributions.

Cons:

Doesn’t preserve mathematical properties like the mean does.

Less effective for symmetric distributions.

Example:

Data: [10, 12, 100, 11, NA]

Mean = 33.25 (inflated by 100)

Median = 11.5 → more representative → impute with 11.5

What is Regularization and what is Dropout technique?

Regularization is a set of techniques used in machine learning to prevent overfitting.

Overfitting occurs when a model learns the training data too well, including the noise and irrelevant details, leading to poor performance on unseen data. A model that overfits has high variance. Regularization helps the model generalize better to new data by adding constraints or penalties to the model's complexity. This typically involves modifying the learning algorithm or the model architecture.

Various Regularization Techniques:

1. L1 Regularization (Lasso Regularization):

- Adds a penalty term to the loss function proportional to the absolute value of the weights.

 - Penalty = λ * Σ|w_i|

- λ is the regularization strength hyperparameter.

- Tends to push some weights to exactly zero, effectively performing feature selection

  by eliminating features that are less important.

2. L2 Regularization (Ridge Regularization):

- Adds a penalty term to the loss function proportional to the square of the magnitude of the weights.

- Penalty = λ * Σ(w_i)^2

-  Tends to shrink weights towards zero but rarely makes them exactly zero.

 - Discourages large weights, leading to a smoother decision boundary and reducing sensitivity to individual data points.

3. Dropout:

- A regularization technique specifically for neural networks.

- During training, randomly sets a fraction of neurons in a layer to zero for each training sample.

- The 'rate' (e.g., 0.5) is the probability of a neuron being dropped out.

- Forces the network to learn more robust features that are not reliant on the presence of  any single neuron. It can be seen as training an ensemble of sub-networks.

 Early Stopping:

- Monitors the model's performance on a validation set during training.

- Training is stopped when the performance on the validation set starts to degrade, even if the performance on the training set is still improving.

- Prevents the model from training for too long and overfitting the training data.

Data Augmentation:

- Creating new training data by applying transformations to the existing data

#      (e.g., rotating images, adding noise to text, scaling sensor readings).

- Increases the size and diversity of the training set, making the model more robust to variations in the input data.

Batch Normalization:

 A technique applied to the output of a layer's activation function (or before the activation).

- Normalizes the activations of each mini-batch by subtracting the batch mean and dividing by the batch standard deviation.

- Helps stabilize the learning process, allows for higher learning rates, and can act as a regularizer by adding a small amount of noise.

Why Dropout is one of them?

Dropout is considered a regularization technique because it directly addresses the problem of overfitting in neural networks by reducing the model's reliance on specific neurons and their correlations.

How Dropout Helps:

 **Prevents Co-adaptation:** Without dropout, neurons might co-adapt, meaning they become overly dependent on specific combinations of other neurons' activations. This can lead to a network that only works well for the exact patterns in the training data. Dropout breaks these dependencies by randomly switching off neurons, forcing remaining neurons to learn more independent and robust features. 

**Ensemble Effect:** Training with dropout can be seen as training an ensemble of many different smaller neural networks. Each time a different set of neurons is dropped out, a slightly different network is trained. At test time (when dropout is typically turned off), the predictions are effectively an average over the predictions of these different sub-networks, which generally leads to better generalization and reduced variance. 

**Reduces Sensitivity to Noise:** By forcing the network to learn features that are useful even when some inputs are missing (due to dropout), the model becomes less sensitive to noise in the training data. 

**Simplified Model (Effectively):** While the total number of parameters remains the same, at any given training step, a smaller, "thinned" network is being used. This effectively reduces the complexity of the model being trained at that moment, which can help prevent overfitting. 

What is loss function and and optimiser in neural network.

The loss function measures how well the model's predictions match the actual target values. The goal during training is to minimize this loss.

Choice: 'binary_crossentropy'

Justification:

- This is a binary classification problem (predicting between two classes: 0 or 1).

- The output layer uses a sigmoid activation function, which outputs a probability between 0 and 1.

- Binary cross-entropy is the standard loss function for binary classification tasks where the output layer uses sigmoid activation. It penalizes the model based on the discrepancy between the predicted probability and the true binary label (0 or 1). It works well when the target is a probability distribution (which is implicitly the case when your target is 0 or 1).

**Optimizer:**

The optimizer is an algorithm used to update the weights of the neural network during training to minimize the loss function. It determines how the model learns from the data.

Choice: 'adam'

Justification:

- Adam (Adaptive Moment Estimation) is a popular and generally effective optimization algorithm.

- It combines ideas from two other optimizers: AdaGrad and RMSprop.

- It adapts the learning rate for each parameter individually based on the first and second moments of the gradients.

- Adam is known for its robustness to different types of neural network architectures and datasets, and it often converges faster than traditional optimizers like Stochastic Gradient Descent (SGD) with fixed learning rates.

- While other optimizers like RMSprop or SGD with momentum could also work, Adam is often a good default choice for many tasks, including this binary classification problem.

**Metrics:**

Metrics are used to evaluate the performance of the model during training and testing. While the loss function drives the optimization, metrics provide more intuitive measures of performance.

Choice: ['accuracy']

Justification:

- Accuracy is the proportion of correctly classified samples. It's a common and easily interpretable metric for classification problems.

- However, given the potential class imbalance (as noted in step 2), accuracy alone might be misleading. More appropriate metrics for imbalanced datasets often include precision, recall, F1-score, or AUC (Area Under the ROC Curve). We will use accuracy for simplicity in compilation but should evaluate with other metrics later.

Other Loss Functions for Neural Networks:

The choice of loss function depends heavily on the type of problem:

- Mean Squared Error (MSE): Used for regression problems. Measures the average of the squared differences between predicted and actual values.

- Mean Absolute Error (MAE): Used for regression problems. Measures the average of the absolute differences between predicted and actual values. Less sensitive to outliers than MSE.

- Categorical Crossentropy: Used for multi-class classification problems where the labels are one-hot encoded (e.g., [0, 1, 0] for class 1 in a 3-class problem).

- Sparse Categorical Crossentropy: Used for multi-class classification problems where the labels are integers (e.g., 1 for class 1). It is equivalent to Categorical Crossentropy but is more convenient when labels are not one-hot encoded.

- Kullback-Leibler Divergence (KL Divergence): Measures the difference between two probability distributions. Used in tasks like generative modeling (e.g., Variational Autoencoders).

- Hinge Loss: Primarily used for Support Vector Machines (SVMs), but can also be used for neural networks in binary classification. It encourages a margin between the decision boundary and the data points.

Other Optimizers Available for Neural Networks:

Numerous optimizers exist, each with different approaches to updating parameters:

- Stochastic Gradient Descent (SGD): The basic optimizer. Updates parameters in the direction opposite to the gradient of the loss function. Can be slow and oscillate around the minimum. Often used with momentum and learning rate schedules.

- SGD with Momentum: Adds a "momentum" term that accumulates gradients over time, helping to accelerate convergence in the correct direction and dampen oscillations.

- Adagrad (Adaptive Gradient): Adapts the learning rate for each parameter based on the historical squared gradients. Parameters with larger gradients get smaller updates, and parameters with smaller gradients get larger updates. Can cause the learning rate to become very small over time.

- Adadelta: An extension of Adagrad that attempts to address the problem of the learning rate diminishing too quickly. It uses a decaying average of squared gradients and squared parameter updates.

- RMSprop (Root Mean Square Propagation): Similar to Adagrad but uses a decaying average of squared gradients, which helps prevent the learning rate from becoming too small.

- Adamax: A variant of Adam based on the infinity norm.

- Nadam (Nesterov-accelerated Adaptive Moment Estimation): Combines Adam with Nesterov momentum, which looks ahead in the gradient direction before updating.

Choosing the right optimizer and loss function is crucial for effective neural network training. The choice is driven by the type of machine learning task (classification, regression, etc.), the nature of the output (binary, multi-class, continuous), and the characteristics of the dataset. While Adam is a good general-purpose optimizer, experimenting with others or tuning their hyperparameters can sometimes lead to better performance.

Where are Loss functions and Optimizers used in which Layer of the neural network?

Loss Function:

The loss function is not directly applied to a specific layer within the neural network. Instead, the loss function is calculated *after* the network has produced its final output from the output layer. It takes the output of the model (usually probabilities or predicted values) and the true target values to compute a single scalar value representing the error or discrepancy. This loss value is then used by the optimizer.

Optimizer:

The optimizer operates on the *entire* network's trainable parameters (weights and biases). It doesn't work on a specific layer in isolation. Based on the calculated loss, the optimizer computes the gradient of the loss with respect to *all* trainable parameters in *all* layers that have trainable weights. This is done through a process called backpropagation. The optimizer then uses these gradients to update the weights and biases in each layer, attempting to minimize the overall loss. So, the optimizer affects the parameters of all layers that contribute to the model's output and have trainable weights.

How to initialise a neural network with Hidden layer and activation function as ReLU, Explain param at each layer.

# Determine the number of input features

input_dim = X_train.shape[1]

# Build the Sequential model

model = Sequential([

    # First hidden layer with ReLU activation

    Dense(64, activation='relu', input_shape=(input_dim,)),

    # Second hidden layer with ReLU activation

    Dense(32, activation='relu'),

    # Output layer with Sigmoid activation for binary classification

    Dense(1, activation='sigmoid')

])

Why First hidden layer is having 64 and second layer as 32 and output layer 1 ?

The choice of layer sizes (64, 32, 1) is somewhat arbitrary and often determined through experimentation  and based on the complexity of the problem and dataset size.

Input Layer: The number of neurons in the input layer is determined by the number of features in your dataset.

In this case, we have 10 features, so the input layer effectively has 10 neurons (though it's implicitly defined by the input_shape in the first Dense layer).

First Hidden Layer (64 neurons): Starting with a larger number of neurons (like 64) in the first hidden layer allows the network to learn a rich set of initial representations from the raw input features. It provides enough capacity to capture various patterns and combinations within the data.

Second Hidden Layer (32 neurons): Reducing the number of neurons in the second hidden layer (to 32) is a common practice. This layer learns more abstract and compressed representations from the output of the first hidden layer. It helps in capturing higher-level patterns and can also help in reducing computational cost and preventing

overfitting by forcing the network to learn more compact representations. The idea is to progressively reduce the dimensionality and complexity as we move deeper into the network, extracting more meaningful features.

Output Layer (1 neuron): For a binary classification problem (like predicting 0 or 1), the output layer needs to produce a single value that can be interpreted as the probability of belonging to one of the classe A single neuron with a sigmoid activation function outputs a value between 0 and 1, which represents the estimated probability of the positive class (target = 1). If the output is > 0.5, the prediction is typically classified as 1, otherwise as 0.

In summary, the numbers 64 and 32 are common starting points for hidden layer sizes in many neural network architectures. They provide sufficient capacity for many tasks without being excessively large, which could lead to overfitting on smaller datasets. The output layer size is dictated by the nature of the prediction task (1 for binary classification, number of classes for multi-class classification, etc.).

Now if we print the summary of the model, it looks below. How the number of params calculated? 

Explanation of Parameter Calculation:

Total parameters in a Dense layer are calculated as:

(number of neurons in previous layer + 1) * number of neurons in current layer

The '+ 1' accounts for the bias term for each neuron in the current layer. 

Layer 1 (Dense, 64 neurons, ReLU):

Input layer has X_train.shape[1] features (which is 10).

Parameters = (number of inputs + 1) * number of neurons

Parameters = (10 + 1) * 64 = 11 * 64 = 704

These are the weights connecting the 10 input features and 1 bias to the 64 neurons.

Layer 2 (Dense, 32 neurons, ReLU):

Previous layer (Layer 1) has 64 neurons.

Parameters = (number of neurons in previous layer + 1) * number of neurons

Parameters = (64 + 1) * 32 = 65 * 32 = 2080

These are the weights connecting the 64 neurons of the first hidden layer and 1 bias to the 32 neurons of the second hidden layer.

Layer 3 (Dense, 1 neuron, Sigmoid):

Previous layer (Layer 2) has 32 neurons.

Parameters = (number of neurons in previous layer + 1) * number of neurons

Parameters = (32 + 1) * 1 = 33 * 1 = 33

These are the weights connecting the 32 neurons of the second hidden layer and 1 bias to the single output neuron.

Total parameters = Parameters from Layer 1 + Parameters from Layer 2 + Parameters from Layer 3

Total parameters = 704 + 2080 + 33 = 2817

The model summary confirms this total number of parameters.

Why Feature scaling is important for neural networks because?

 1. Gradient Descent Convergence: Features with larger scales can dominate the gradient calculation,

    leading to slower convergence and potentially getting stuck in local minima. Scaling brings all

    features to a similar range, allowing the optimization algorithm to find the minimum more efficiently.

 2. Activation Functions: Many activation functions (like sigmoid or tanh) are sensitive to the input

    range. Large input values can lead to saturation, where the gradient becomes very small, hindering

    learning. Scaling prevents this saturation by keeping inputs within a reasonable range.

 3. Weight Initialization: Proper weight initialization techniques assume that input features are scaled.

    If features have vastly different scales, the initial weights might not be appropriate, leading

    to instability during training.

 4. Regularization Techniques: Some regularization techniques (like L2 regularization) penalize large

    weights. If features are not scaled, the model might be forced to assign large weights to features

    with larger scales, disproportionately affecting the regularization penalty.

One more aspect of this is below 

Why Feature Scaling is Important

Faster Convergence

Neural networks optimize using gradient descent.

If features are on different scales, gradients can oscillate and take longer to converge.

Avoids Exploding/Vanishing Gradients

Large feature values can lead to exploding gradients

Very small feature values can lead to vanishing gradients.

Better Weight Initialization

Neural networks assume inputs are centered around 0 (especially with activations like tanh or ReLU).

If features vary drastically, some neurons may become ineffective (e.g., stuck ReLUs).

Equal Contribution from Features

Without scaling, features with larger ranges dominate the loss function and bias the model unfairly.

Simple neural network example

#Initializing the neural network

model = Sequential()

model.add(Dense(1,input_dim=x_train.shape[1]))

model.summary()

optimizer = keras.optimizers.SGD()    # defining SGD as the optimizer to be used

model.compile(loss="mean_squared_error", optimizer=optimizer, metrics=metrics,run_eagerly=True)

epochs = 10

batch_size = x_train.shape[0]

start = time.time()

history = model.fit(x_train, y_train, validation_data=(x_val,y_val) , batch_size=batch_size, epochs=epochs)

end=time.time()

plot(history,'loss')

plot(history,'r2_score')

results.loc[0]=['-','-','-',epochs,batch_size,'GD',(end-start),history.history["loss"][-1],history.history["val_loss"][-1],history.history["r2_score"][-1],history.history["val_r2_score"][-1]]

What are various activation functions in Deep Learning?

1. Linear Function (Identity Function)

Formula: f(x)=x

Description: The output is directly proportional to the input. It's a straight line.

When Used:

Output Layer of Regression Models: When predicting a continuous numerical value (e.g., house price, temperature).

Occasionally in Intermediate Layers (rarely): While theoretically possible, using only linear activations throughout a deep network would make the entire network equivalent to a single linear transformation, losing the ability to learn complex patterns.

Advantages:

Simple to understand and implement.

No vanishing/exploding gradient problems when used as the only activation.

Disadvantages:

Cannot learn non-linear relationships.

A neural network with only linear activation functions can only learn a linear function, regardless of the number of layers.

2. Binary Step Function

Formula: f(x)=1 if x≥0 f(x)=0 if x<0

Description: Outputs a binary value (0 or 1) based on whether the input crosses a certain threshold (usually 0).

When Used:

Historical Significance: Primarily used in early perceptrons for binary classification tasks.

Not in Modern Deep Learning: Rarely used in hidden layers of modern neural networks due to its limitations.

Advantages:

Simple and computationally inexpensive.

Clear binary output.

Disadvantages:

Non-differentiable at 0: This means gradient-based optimization methods (like backpropagation) cannot be directly applied.

Zero gradient elsewhere: Gradients are 0 for all other inputs, meaning the weights cannot be updated if the input is not exactly 0.

Cannot handle multi-class problems well.

3. Non-Linear Activation Functions (General Advantages)

All the following functions are non-linear. The primary advantage of non-linear activation functions is that they allow neural networks to learn and approximate complex, non-linear relationships in data. Without non-linearity, a multi-layered neural network would essentially behave like a single-layered network, limiting its representational power. They enable the network to learn intricate patterns and solve non-linear classification and regression problems.

4. Sigmoid (Logistic)

Formula: f(x)= 

1+e 

−x

1

Description: Squashes the input value into a range between 0 and 1. It has an "S" shape.

When Used:

Output Layer for Binary Classification: When you need a probability-like output between 0 and 1 (e.g., predicting the probability of an email being spam).

Historically in Hidden Layers: Was popular in hidden layers but has largely been replaced by ReLU and its variants.

Advantages:

Output is normalized between 0 and 1, suitable for probabilities.

Smooth gradient, which prevents "jumps" in output values.

Disadvantages:

Vanishing Gradient Problem: Gradients are very small for very large positive or negative inputs, leading to slow or halted learning in deep networks.

Outputs are not zero-centered: This can cause issues with gradient updates, leading to a "zig-zagging" optimization path.

Computationally expensive compared to ReLU.

5. TanH (Hyperbolic Tangent)

Formula: f(x)= 

e 

x

 +e 

−x

 

e 

x

 −e 

−x

 

​

  (or tanh(x))

Description: Squashes the input value into a range between -1 and 1. Also has an "S" shape, centered at 0.

When Used:

Hidden Layers: More often used in hidden layers than Sigmoid, particularly in older architectures or recurrent neural networks (RNNs) where it can be beneficial due to its zero-centered output.

Advantages:

Zero-centered output: This is a significant advantage over Sigmoid, as it helps alleviate the zig-zagging effect during gradient descent and makes training more stable.

Stronger gradients than Sigmoid for values closer to 0.

Disadvantages:

Still suffers from Vanishing Gradient Problem: Similar to Sigmoid, gradients become very small for large positive or negative inputs.

Computationally more expensive than ReLU.

6. ReLU (Rectified Linear Unit)

Formula: f(x)=max(0,x)

Description: Outputs the input directly if it's positive, otherwise outputs 0. It's a simple piecewise linear function.

When Used:

Most Common Choice for Hidden Layers: The default activation function for hidden layers in the vast majority of deep learning models (Convolutional Neural Networks, Feedforward Networks, etc.).

Advantages:

Solves Vanishing Gradient Problem (for positive inputs): The gradient is 1 for positive inputs, preventing saturation.

Computationally Efficient: Simple to compute and its derivative is also simple (0 or 1).

Sparsity: Can lead to sparse activations (some neurons output 0), which can be beneficial for efficiency and representation.

Disadvantages:

Dying ReLU Problem: Neurons can become "dead" if their input is always negative, causing their gradient to be 0. Once a neuron outputs 0, it never updates its weights via backpropagation.

Outputs are not zero-centered.

7. Leaky ReLU

Formula: f(x)=x if x≥0, else f(x)=αx (where α is a small positive constant, e.g., 0.01)

Description: Similar to ReLU, but instead of outputting 0 for negative inputs, it outputs a small linear component.

When Used:

Hidden Layers: Used as an alternative to ReLU when the dying ReLU problem is a concern.

Advantages:

Mitigates Dying ReLU Problem: By providing a small gradient for negative inputs, it allows neurons to "recover" and continue learning.

Computationally efficient.

Disadvantages:

Performance is not always consistent and can vary.

The choice of α is often heuristic.

8. Parametric ReLU (PReLU)

Formula: f(x)=x if x≥0, else f(x)=αx (where α is a learnable parameter)

Description: An extension of Leaky ReLU where the slope α for negative inputs is learned during training via backpropagation, rather than being a fixed hyperparameter.

When Used:

Hidden Layers: Can be used in architectures where fine-tuning the negative slope might lead to better performance.

Advantages:

Learns the optimal slope: Allows the model to adapt the activation function to the specific data, potentially leading to better performance.

Addresses the dying ReLU problem.

Disadvantages:

Adds an additional parameter to learn per neuron, slightly increasing model complexity.

Might be prone to overfitting if not enough data is available.

9. Exponential Linear Unit (ELU)

Formula: f(x)=x if x≥0, else f(x)=α(e 

x

 −1) (where α is a positive constant, often 1)

Description: For positive inputs, it's linear like ReLU. For negative inputs, it smoothly curves towards −α.

When Used:

Hidden Layers: Can be used as an alternative to ReLU and its variants, particularly in deep networks.

Advantages:

Addresses Dying ReLU: The negative values allow for non-zero gradients, preventing dead neurons.

Smoother transition: The exponential curve for negative inputs leads to more robust learning, especially when inputs are slightly negative.

Closer to zero-centered output: For inputs below zero, it converges to −α, pulling the mean activation closer to zero, which can lead to faster learning.

Disadvantages:

Computationally more expensive than ReLU due to the exponential function.

10. Swish

Formula: f(x)=x⋅sigmoid(βx) (often β=1, so f(x)=x⋅sigmoid(x))

Description: A smooth, non-monotonic function that is a product of the input and the sigmoid of the input. It's "self-gated."

When Used:

Hidden Layers: Demonstrated to outperform ReLU in some deeper models, notably in architectures like EfficientNet.

Advantages:

Smooth and Non-monotonic: The non-monotonicity (a dip below zero before rising) can sometimes help with learning complex patterns.

Better performance in deep networks: Often found to yield better results than ReLU in very deep models.

Avoids the dying ReLU problem.

Disadvantages:

Computationally more expensive than ReLU due to the sigmoid function.

11. Maxout

Formula: f(x)=max(w 

1

T

​

 x+b 

1

​

 ,w 

2

T

​

 x+b 

2

​

 ,…,w 

k

T

​

 x+b 

k

​

 )

Description: Instead of a fixed function, a Maxout unit takes the maximum of k linear functions. It's a generalization of ReLU (ReLU is a Maxout unit with one linear function being 0 and the other being x).

When Used:

Hidden Layers: Can be used in deep networks, often alongside dropout.

Advantages:

Approximates any convex function: This makes it a very powerful and expressive activation function.

Does not suffer from dying ReLU: Since it's the maximum of linear functions, the gradient will always be non-zero for at least one of the linear functions.

No vanishing/exploding gradients (due to its piecewise linear nature).

Disadvantages:

Increases number of parameters: Each Maxout unit has k times more parameters than a standard ReLU unit, significantly increasing model complexity and training time.

Computationally more expensive during inference due to evaluating multiple linear functions.

12. Softmax

Formula: For an input vector z=[z 

1

​

 ,z 

2

​

 ,…,z 

K

​

 ], the softmax function outputs a probability distribution σ(z) 

i

​

 = 

∑ 

j=1

K

​

 e 

z 

j

​

 

 

e 

z 

i

​

 

 

​

 

Description: Converts a vector of arbitrary real values into a probability distribution, where each value is between 0 and 1, and all values sum to 1.

When Used:

Output Layer for Multi-class Classification: This is its primary and almost exclusive use. It's used when a data point belongs to exactly one of several possible classes (e.g., classifying an image as a cat, dog, or bird).

Advantages:

Provides a probability distribution: The output directly represents the confidence scores for each class.

Numerically stable: Exponentiation makes it suitable for larger inputs.

Disadvantages:

Only suitable for multi-class classification output layers.

Not used in hidden layers.

The evolution of activation functions reflects the continuous effort to overcome limitations like vanishing gradients and improve training stability and performance in deeper neural networks. While ReLU remains the workhorse for many hidden layers due to its simplicity and effectiveness, newer functions like Swish and ELU offer promising alternatives for specific architectures and tasks.

What is KIND Tool?

Although Kubernetes production clusters are typically in a cloud environment, with the right tool,  running a Kubernetes cluster locally is not only possible but can also provide a variety of key benefits such as accelerated productivity, easy and efficient testing, and reduced resource expenditure. 

Kubernetes-in-Docker (Kind) is a command-line tool that enables developers to create a local Kubernetes cluster using docker images. With this novel approach, users can take advantage of Docker’s straightforward, self-contained deployments and cleanup to create and test Kubernetes infrastructure without the operational overhead of a full-blown cluster. 

The first step to understanding Kind and the value it brings to the table is to understand why developers would want a local Kubernetes development solution. There are a number of reasons to utilize a local Kubernetes cluster, for instance, the ability to test deployment methods check how the application interacts with mounted volumes, and test manifest files. 

It’s not enough for developers to simply spin up a service and test it. As services are deployed to Kubernetes clusters, developers must ensure they work together with other services and communicate properly with each other. Because of this, today it is more important than ever to have the option to run a Kubernetes cluster locally.

Here are some key use cases in which local Kubernetes clusters can be particularly beneficial: 

Proof of concepts and experimentation: Using local environments eliminates the need to provide the required cloud infrastructure, security configurations, and other administrative tasks. In essence, developers will be able to experiment and carry out Proof of Concepts (POCs) in a low-risk environment.

Smaller teams: With the differences in local machines and their respective software and configuration setups, there is a greater chance of configuration drift in large teams. However, a smaller team of experienced Kubernetes developers will be better able to standardize and align their cluster configurations based on the hardware being used, making local clusters more suitable. 

Low computation requirements: Local clusters are best suited for development environments with low computation requirements, or in other words, “simple” applications. 

What is Kind?

Kind is an open-source, CNCF-certified Kubernetes installer used by developers to quickly and easily create Kubernetes clusters using Docker container “nodes.” Though primarily designed for testing Kubernetes itself, Kind has proven to be an adept tool for local development and continuous integration (CI) pipelines. 

How does Kind work? 

At a high level, Kind clusters can be visualized as a single Docker container that runs a control plane node and worker nodes to form a Kubernetes cluster. Essentially, Kind bundles every Kubernetes object into a single image (called a node image), that contains all the required Kubernetes components to create a single-node or multi-node cluster. 

Kind creates images, however, developers have the option to create their own image if needed. Once the Kubernetes cluster is created, kind automatically configures kubectl context, making deployment easy and robust.

Support for multi-node clusters (including HA).

Support for building Kubernetes release builds from source.

Support for make/bash, docker, or bazel, in addition to pre-published builds.

Can be configured to run various releases of Kubernetes (v1.16.3, v1.17.1, etc.)

Kind is far from the only solution for running local clusters in Kubernetes, yet despite competing against tools such as Minkube, K3s, Microk8s, and more, Kind remains a strong contender in the market. 

Simplicity. With Kind, it’s simple to set up a Kubernetes environment for local testing without needing virtual machines or anything more complicated than a Docker install. Using the tool, developers can easily create, recreate or delete a cluster with a single command. Additionally, kind enables developers to load local container images directly into the Kubernetes cluster, saving the time and effort needed to set up a registry and push the images repeatedly. 

Speed. One of the key advantages of Kind is its start-up time, which is significantly faster than similar tools such as Minikube. For instance, Kind can launch a fully compliant Kubernetes cluster using Docker containers as nodes in less than a minute, drastically improving the developer experience when testing against Kubernetes. 

Customization. Another benefit of Kind is the customization it offers. By default, Kind creates the cluster with only one node, which acts as a control plane, however, users have the option to configure kind to run in a multi-node setup and add multiple control planes to simulate high availability. Additionally, because Kind works with docker images, developers can specify a custom docker image they want to run. 

 

references:

https://www.devoteam.com/expert-view/kind-simplifying-kubernetes-testing/#:~:text=Kind%20is%20an%20open%2Dsource,continuous%20integration%20(CI)%20pipelines.

High level overview of MCP components

At its core, MCP follows a client-server architecture where a host application can connect to multiple servers.

MCP hosts - apps like Claude Desktop, Cursor, Windsurf, or AI tools that want to access data via MCP.

MCP Clients - protocol clients that maintain 1:1 connections with MCP servers, acting as the communication bridge.

MCP Servers - lightweight programs that each expose specific capabilities (like reading files, querying databases...) through the standardized Model Context Protocol.

Local Data Sources - files, databases, and services on your computer that MCP servers can securely access. For instance, a browser automation MCP server needs access to your browser to work.

Remote Services - External APIs and cloud-based systems that MCP servers can connect to.

What is main difference between Kubernetes and Openshift

People move from Kubernetes to OpenShift for several reasons, including ease of use, built-in tools, and enhanced security features. OpenShift simplifies Kubernetes management by providing a user-friendly interface and integrating CI/CD pipelines, making it easier for teams to deploy and manage applications. OpenShift also offers additional features like integrated development tools, image stream management, and centralized policy management, which can streamline development and operations. 

Here's a more detailed look at the reasons for migrating to OpenShift:

Ease of Use and Management:

OpenShift simplifies Kubernetes by providing a user-friendly interface and built-in tools for CI/CD, image management, and policy enforcement. This can significantly reduce the time and effort required to manage and operate Kubernetes clusters, particularly for teams without extensive Kubernetes expertise. 

Enhanced Security:

OpenShift offers a robust security framework, including role-based access control, network policies, and security contexts, which can help ensure the security of containerized applications. It also provides built-in security and encryption for container communications. 

Integrated Tools:

OpenShift includes a variety of integrated tools, such as source-to-image (S2I) for faster application development, image streams for container image management, and centralized policy management, which can streamline development workflows. 

Scalability and Customization:

OpenShift allows for customized scalability options to meet specific business needs, building upon the automated scaling capabilities of Kubernetes. 

On-Premise and Edge Computing:

OpenShift is well-suited for on-premise deployments and edge computing environments, offering robust security and management features in these environments. 

Red Hat Support:

OpenShift is backed by Red Hat's commercial support, which can be valuable for organizations that require a vendor-supported platform. 

Virtualization Integration:

OpenShift integrates with virtualization technologies like VMware and offers a unified platform for managing both virtualized and containerized workloads. 

In essence, OpenShift provides a more complete and user-friendly container management platform compared to bare-bones Kubernetes, offering a combination of enhanced security, integrated tools, and simplified management capabilities that can be particularly appealing to organizations seeking to streamline their cloud-native development and operations. 

What is Auto regressive decoding in LLMs

Auto-regressive decoding is the fundamental process by which most large language models (LLMs) generate text, one token at a time, in a sequential manner. The core idea is that the model predicts the next token based on all the tokens that have been generated so far, including the initial input prompt.

Here's a breakdown of how it works:

The Process:

Input: The process starts with an input prompt, which is a sequence of tokens.

Encoding: The LLM first processes this input prompt, typically by converting the tokens into numerical representations called embeddings.

First Token Prediction: Based on the encoded prompt, the model predicts the probability distribution over its entire vocabulary for the next token.

Token Sampling/Selection: A decoding strategy is then used to select the next token from this probability distribution. Common strategies include:

Greedy Decoding: Simply selecting the token with the highest probability. This is fast but can lead to repetitive or suboptimal outputs.

Sampling: Randomly selecting a token based on its probability. This introduces more diversity but can also lead to less coherent outputs.

Beam Search: Keeping track of multiple promising candidate sequences (beams) and expanding them at each step. This often yields better quality text than greedy decoding but is more computationally expensive.

Appending the Token: The selected token is appended to the currently generated sequence.

Iteration: The model then takes the original prompt plus the newly generated token as the new input and repeats steps 3-5 to predict the next token.

Stopping Condition: This process continues until a predefined stopping condition is met, such as reaching a maximum sequence length or generating a special "end-of-sequence" token.

Output: The final sequence of generated tokens is then converted back into human-readable text.

Why is it called "Auto-regressive"?

The term "auto-regressive" comes from statistics and signal processing. In this context, it means that the model's output at each step is dependent on its own previous outputs. The model "regresses" on its own generated history to predict the future.

Key Characteristics:

Sequential Generation: Tokens are generated one after the other. This inherent sequential nature can be a bottleneck for inference speed, especially for long sequences.

Context Dependency: Each predicted token is conditioned on the entire preceding context. This allows the model to maintain coherence and relevance in its generated text.

Probability Distribution: At each step, the model outputs a probability distribution over the vocabulary, allowing for different decoding strategies to influence the final output.

Implications:

Inference Speed: The sequential nature of auto-regressive decoding is a primary factor contributing to the latency of LLM inference. Generating longer sequences requires more steps.

Computational Cost: Each decoding step involves a forward pass through the model, which can be computationally intensive for large models.

Decoding Strategy Impact: The choice of decoding strategy significantly affects the quality, diversity, and coherence of the generated text, as well as the inference speed.

In summary, auto-regressive decoding is the step-by-step process of generating text by predicting one token at a time, with each prediction being conditioned on the previously generated tokens and the initial input. It's a fundamental mechanism behind the impressive text generation capabilities of modern LLMs.

References 

Gemini 

What is Cost, Throughput, Latency of LLM Inference? What are factors affecting these two and how to compute these ?

Throughput = Query / sec => Maximise for batch job speed or to allow more users 

Latency = sec / token => minimise for user experience ( how fast the application will look) = users can read 200 words per sec, so as long as the latency is below this, application will look to be performing good. 

Cost : Cheaper is better 

Let's break down the cost, throughput, and latency of LLM inference.

Cost of LLM Inference

What it is: The expense associated with running an LLM to generate responses or perform tasks based on input prompts.

Factors Affecting Cost:

Model Size: Larger models with more parameters generally require more computational resources, leading to higher costs.

Number of Tokens: Most LLM APIs charge based on the number of input and output tokens processed. Longer prompts and longer generated responses increase costs. Output tokens are often more expensive than input tokens.

Complexity of the Task: More complex tasks might require more processing and thus incur higher costs.

Hardware Used: The type and amount of hardware (GPUs, CPUs) used for inference significantly impact costs, especially for self-hosted models. Cloud-based services abstract this but factor it into their pricing.

Pricing Model: Different providers have varying pricing models (per token, per request, compute time, etc.).

Model Provider: Different providers offer the same or similar models at different price points.

Mixture of Experts (MoE) Models: These models might be priced based on the total number of parameters or the number of active parameters during inference.

How to Compute Cost:

The cost calculation depends on the pricing model of the LLM service or the infrastructure cost if self-hosting.

Per Token Pricing (Common for API services):

Cost = (Input Tokens / 1000 * Input Price per 1k tokens) + (Output Tokens / 1000 * Output Price per 1k tokens)

Self-Hosting: This involves calculating the cost of hardware (amortized over time), electricity, data center costs, and potentially software licenses. This is more complex and depends on your specific infrastructure.

Cloud Inference Services: These typically provide a per-token cost, and you can estimate based on your expected token usage. Some might have per-request fees as well.

Key Considerations:

Input vs. Output Tokens: Be mindful of the different costs for input and output tokens.

Context Length: Longer context windows can lead to higher token usage and thus higher costs.

Tokenization: Different models tokenize text differently, affecting the number of tokens for the same input.

Throughput of LLM Inference

What it is: The rate at which an LLM can process inference requests. It's often measured in:

Tokens per second (TPS): The number of input and/or output tokens the model can process or generate in one second. This is a common metric.

Requests per second (RPS): The number of independent inference requests the model can handle in one second. This depends on the total generation time per request.

Factors Affecting Throughput:

Model Size and Architecture: Smaller, less complex models generally have higher throughput.

Hardware: More powerful GPUs or CPUs with higher memory bandwidth lead to higher throughput. Using multiple parallel processing units (GPUs) significantly increases throughput.

Batch Size: Processing multiple requests together (batching) can significantly improve throughput by better utilizing the hardware. However, very large batch sizes can increase latency due to memory limitations.

Input/Output Length: Shorter prompts and shorter generated responses lead to higher throughput.

Optimization Techniques: Techniques like quantization, pruning, key-value caching, and efficient attention mechanisms (e.g., FlashAttention, Group Query Attention) can significantly boost throughput.

Parallelism: Techniques like tensor parallelism and pipeline parallelism distribute the model and its computations across multiple devices, improving throughput for large models.

Memory Bandwidth: The speed at which data (model weights, activations) can be transferred to the processing units is a crucial bottleneck for throughput.

How to Compute Throughput:

Tokens per Second:

Measure the total number of tokens processed (input + output) or generated (output only) over a specific time period.

TPS= 

Total Time (in seconds)

Total Tokens

​

 

Requests per Second:

Measure the total number of inference requests completed over a specific time period.

RPS= 

Total Time (in seconds)

Total Requests

​

 

RPS is also related to the average total generation time per request:

RPS≈ 

Average Total Generation Time per Request

1

​

 

Key Considerations:

Input vs. Output Tokens: Specify whether the throughput refers to input, output, or the sum of both.

Concurrency: Throughput often increases with the number of concurrent requests, up to a certain point.

Latency Trade-off: Increasing batch size to improve throughput can increase the latency for individual requests.

Latency of LLM Inference

What it is: The delay between sending an inference request to the LLM and receiving the response. It's a critical factor for user experience, especially in real-time applications. Common metrics include:

Time to First Token (TTFT): The time it takes for the model to generate the very first token of the response after receiving the prompt. This is crucial for perceived responsiveness.

Time Per Output Token (TPOT) / Inter-Token Latency (ITL): The average time taken to generate each subsequent token after the first one.

Total Generation Time / End-to-to-End Latency: The total time from sending the request to receiving the complete response.

Total Generation Time = TTFT + (TPOT * Number of Output Tokens)

Factors Affecting Latency:

Model Size and Complexity: Larger models generally have higher latency due to the increased computations required.

Input/Output Length: Longer prompts require more processing time before the first token can be generated (affecting TTFT). Longer desired output lengths naturally increase the total generation time.

Hardware: Faster GPUs or CPUs with lower memory access times reduce latency.

Batch Size: While batching improves throughput, it can increase the latency for individual requests as they wait to be processed in a batch.

Optimization Techniques: Model compression (quantization, pruning) and optimized attention mechanisms can reduce the computational overhead and thus lower latency.

Network Conditions: For cloud-based APIs, network latency between the user and the inference server adds to the overall latency. Geographical distance to the server matters.

System Load: High load on the inference server can lead to queuing and increased latency.

Cold Starts: The first inference request after a period of inactivity might experience higher latency as the model and necessary data are loaded into memory.

Tokenization: The time taken to tokenize the input prompt also contributes to the initial latency (TTFT).

How to Compute Latency:

Time to First Token (TTFT): Measure the time difference between sending the request and receiving the first token.

Time Per Output Token (TPOT): Measure the time taken to generate the entire response (excluding TTFT) and divide it by the number of output tokens.

TPOT= 

Number of Output Tokens

Total Generation Time - TTFT

​

 

Total Generation Time: Measure the time difference between sending the request and receiving the last token of the response.

Key Considerations:

TTFT Importance: For interactive applications, minimizing TTFT is crucial for a good user experience.

Trade-off with Throughput: Optimizations for higher throughput (like large batch sizes) can negatively impact latency.

Variability: Latency can vary depending on the specific prompt, the model's state, and the server load. It's often useful to measure average and percentile latencies.

Understanding these three aspects – cost, throughput, and latency – and the factors that influence them is crucial for effectively deploying and utilizing LLMs in various applications. There's often a trade-off between these metrics, and the optimal balance depends on the specific use case and requirements.

Creating Knowledge Graph using SimpleKGPipeline

Below are the properties of the graph we are creating 

Document: metadata for document sources

Chunk: text chunks from the documents with embeddings to power vector retrieval

__Entity__: Entities extracted from the text chunks

Creating a knowledge graph with the GraphRAG Python package is pretty simple

The SimpleKGPipeline class allows you to automatically build a knowledge graph with a few key inputs, including

a driver to connect to Neo4j,

an LLM for entity extraction, and

an embedding model to create vectors on text chunks for similarity search.

Neo4j Driver

The Neo4j driver allows you to connect and perform read and write transactions with the database. You can obtain the URI, username, and password variables from when you created the database. If you created your database on AuraDB, they are in the file you downloaded.

import neo4j

neo4j_driver = neo4j.GraphDatabase.driver(NEO4J_URI,

                                         auth=(NEO4J_USERNAME, NEO4J_PASSWORD))

LLM & Embedding Model

In this case, we will use OpenAI GPT-4o-mini for convenience. It is a fast and low-cost model. The GraphRAG Python package supports opens in new tabany LLM model, including models from OpenAI, Google VertexAI, Anthropic, Cohere, Azure OpenAI, local Ollama models, and any chat model that works with LangChain. You can also implement a custom interface for any other LLM.

Likewise, we will use OpenAI’s default text-embedding-ada-002 for the embedding model, but you can use opens in new tabother embedders from different providers.

import neo4j

from neo4j_graphrag.llm import OpenAILLM

from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings

llm=OpenAILLM(

   model_name="gpt-4o-mini",

   model_params={

       "response_format": {"type": "json_object"}, # use json_object formatting for best results

       "temperature": 0 # turning temperature down for more deterministic results

   }

)

#create text embedder

embedder = OpenAIEmbeddings()

Optional Inputs: Schema & Prompt Template

While not required, adding a graph schema is highly recommended for improving knowledge graph quality. It provides guidance for the node and relationship types to create during entity extraction.

Pro-tip: If you are still deciding what schema to use, try building a graph without a schema first and examine the most common node and relationship types created as a starting point.

For our graph schema, we will define entities (a.k.a. node labels) and relations that we want to extract. While we won’t use it in this simple example, there is also an optional potential_schema argument, which can guide opens in new tabwhich relationships should connect to which nodes.

basic_node_labels = ["Object", "Entity", "Group", "Person", "Organization", "Place"]

academic_node_labels = ["ArticleOrPaper", "PublicationOrJournal"]

medical_node_labels = ["Anatomy", "BiologicalProcess", "Cell", "CellularComponent",

                      "CellType", "Condition", "Disease", "Drug",

                      "EffectOrPhenotype", "Exposure", "GeneOrProtein", "Molecule",

                      "MolecularFunction", "Pathway"]

node_labels = basic_node_labels + academic_node_labels + medical_node_labels

# define relationship types

rel_types = ["ACTIVATES", "AFFECTS", "ASSESSES", "ASSOCIATED_WITH", "AUTHORED",

   "BIOMARKER_FOR", …]

We will also be adding a custom prompt for entity extraction. While the GraphRAG Python package has an internal default prompt, engineering a prompt closer to your use case often helps create a more applicable knowledge graph. The prompt below was created with a bit of experimentation. Be sure to follow the same general format as the opens in new tabdefault prompt.

prompt_template = '''

You are a medical researcher tasks with extracting information from papers 

and structuring it in a property graph to inform further medical and research Q&A.

Extract the entities (nodes) and specify their type from the following Input text.

Also extract the relationships between these nodes. the relationship direction goes from the start node to the end node. 

Return result as JSON using the following format:

{{"nodes": [ {{"id": "0", "label": "the type of entity", "properties": {{"name": "name of entity" }} }}],

  "relationships": [{{"type": "TYPE_OF_RELATIONSHIP", "start_node_id": "0", "end_node_id": "1", "properties": {{"details": "Description of the relationship"}} }}] }}

- Use only the information from the Input text. Do not add any additional information.  

- If the input text is empty, return empty Json. 

- Make sure to create as many nodes and relationships as needed to offer rich medical context for further research.

- An AI knowledge assistant must be able to read this graph and immediately understand the context to inform detailed research questions. 

- Multiple documents will be ingested from different sources and we are using this property graph to connect information, so make sure entity types are fairly general. 

Use only fhe following nodes and relationships (if provided):

{schema}

Assign a unique ID (string) to each node, and reuse it to define relationships.

Do respect the source and target node types for relationship and

the relationship direction.

Do not return any additional information other than the JSON in it.

Examples:

{examples}

Input text:

{text}

'''

Creating the SimpleKGPipeline

Create the SimpleKGPipeline using the constructor below:

from neo4j_graphrag.experimental.components.text_splitters.fixed_size_splitter import FixedSizeSplitter

from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline

kg_builder_pdf = SimpleKGPipeline(

   llm=ex_llm,

   driver=driver,

   text_splitter=FixedSizeSplitter(chunk_size=500, chunk_overlap=100),

   embedder=embedder,

   entities=node_labels,

   relations=rel_types,

   prompt_template=prompt_template,

   from_pdf=True

)

Running the Knowledge Graph Builder

You can run the knowledge graph builder with the run_async method. We are going to iterate through 3 PDFs below.

pdf_file_paths = ['truncated-pdfs/biomolecules-11-00928-v2-trunc.pdf',

            'truncated-pdfs/GAP-between-patients-and-clinicians_2023_Best-Practice-trunc.pdf',

            'truncated-pdfs/pgpm-13-39-trunc.pdf']

for path in pdf_file_paths:

    print(f"Processing : {path}")

    pdf_result = await kg_builder_pdf.run_async(file_path=path)

    print(f"Result: {pdf_result}")

Once complete, you can explore the resulting knowledge graph. opens in new tabThe Unified Console provides a great interface for this.

Go to the Query tab and enter the below query to see a sample of the graph.

MATCH p=()-->() RETURN p LIMIT 1000;

Basics of GraphRAG python package

This package contains the official Neo4j GraphRAG features for Python.

The purpose of this package is to provide a first party package to developers, where Neo4j can guarantee long term commitment and maintenance as well as being fast to ship new features and high performing patterns and methods.

 This package is a renamed continuation of neo4j-genai. The package neo4j-genai is deprecated and will no longer be maintained. We encourage all users to migrate to this new package to continue receiving updates and support.

pip install neo4j-graphrag

pip install "neo4j-graphrag[openai]"

LLM providers (at least one is required for RAG and KG Builder Pipeline):

ollama: LLMs from Ollama

openai: LLMs from OpenAI (including AzureOpenAI)

google: LLMs from Vertex AI

cohere: LLMs from Cohere

anthropic: LLMs from Anthropic

mistralai: LLMs from MistralAI

sentence-transformers : to use embeddings from the sentence-transformers Python package

Vector database (to use External Retrievers):

weaviate: store vectors in Weaviate

pinecone: store vectors in Pinecone

qdrant: store vectors in Qdrant

experimental: experimental features mainly from the Knowledge Graph creation pipelines.

nlp:

spaCy: load spaCy trained models for nlp pipelines, used by SpaCySemanticMatchResolver component from the Knowledge Graph creation pipelines.

fuzzy-matching:

rapidfuzz: apply fuzzy matching using string similarity, used by FuzzyMatchResolver component from the Knowledge Graph creation pipelines.

Sample is as below 

Creating the Vector indexes 

===========================

from neo4j import GraphDatabase

from neo4j_graphrag.indexes import create_vector_index

URI = "neo4j://localhost:7687"

AUTH = ("neo4j", "password")

INDEX_NAME = "vector-index-name"

# Connect to Neo4j database

driver = GraphDatabase.driver(URI, auth=AUTH)

# Creating the index

create_vector_index(

    driver,

    INDEX_NAME,

    label="Document",

    embedding_property="vectorProperty",

    dimensions=1536,

    similarity_fn="euclidean",

)

Populating the vector indexes 

===========================

from neo4j import GraphDatabase

from neo4j_graphrag.indexes import upsert_vectors

from neo4j_graphrag.types import EntityType

URI = "neo4j://localhost:7687"

AUTH = ("neo4j", "password")

# Connect to Neo4j database

driver = GraphDatabase.driver(URI, auth=AUTH)

# Upsert the vector

vector = ...

upsert_vectors(

    driver,

    ids=["1234"],

    embedding_property="vectorProperty",

    embeddings=[vector],

    entity_type=EntityType.NODE,

)

Below is how to retrieve the documents 

from neo4j import GraphDatabase

from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings

from neo4j_graphrag.retrievers import VectorRetriever

URI = "neo4j://localhost:7687"

AUTH = ("neo4j", "password")

INDEX_NAME = "vector-index-name"

# Connect to Neo4j database

driver = GraphDatabase.driver(URI, auth=AUTH)

# Create Embedder object

# Note: An OPENAI_API_KEY environment variable is required here

embedder = OpenAIEmbeddings(model="text-embedding-3-large")

# Initialize the retriever

retriever = VectorRetriever(driver, INDEX_NAME, embedder)

# Run the similarity search

query_text = "How do I do similarity search in Neo4j?"

response = retriever.search(query_text=query_text, top_k=5)

references:

https://neo4j.com/docs/neo4j-graphrag-python/current/

SambaNova Reconfigurable Dataflow Architecture

The SambaNova Reconfigurable Dataflow Architecture™ (RDA) is a computing architecture designed to enable the next

generation of machine learning and high performance computing applications. The Reconfigurable Dataflow Architecture

is a complete, full-stack solution that incorporates innovations at all layers including algorithms, compilers, system

architecture and state-of-the-art silicon.

The RDA provides a flexible, dataflow execution model that pipelines operations, enables programmable data access

patterns and minimizes excess data movement found in fixed, core-based, instruction set architectures. It does not have a

fixed Instruction Set Architecture (ISA) like traditional architectures, but instead is programmed specifically for each model

resulting in a highly optimized, application-specific accelerator.

The Reconfigurable Dataflow Architecture is composed of the following:

SambaNova Reconfigurable Dataflow UnitTM is a next-generation processor designed to provide native dataflow processing

and programmable acceleration. It has a tiled architecture that comprises a network of reconfigurable functional units.

The architecture enables a broad set of highly parallelizable patterns contained within dataflow graphs to be efficiently

programmed as a combination of compute, memory and communication networks. 

SambaFlowTM is a complete software stack designed to take input from standard machine-learning frameworks such as

PyTorch and TensorFlow. SambaFlow automatically extracts, optimizes and maps dataflow graphs onto RDUs, allowing high

performance to be obtained without the need for low-level kernel tuning. SambaFlow also provides an API for expert users

and those who are interested in leveraging the RDA for workloads beyond machine learning.

SambaNova Systems DataScaleTM is a complete, rack-level, data-center-ready accelerated computing system. Each

DataScale system configuration consists of one or more DataScale nodes, integrated networking and management

infrastructure in a standards-compliant data center rack, referred to as the SN10-8R.

Progress against the challenges outlined earlier would be limited with an approach the solely focuses on a new silicon

design or algorithm breakthrough. Through an integrated, full-stack solution, SambaNova is able to innovate across layers to

achieve a multiplying effect. Additionally, SambaNova DataScale leverages open standards and common form factors to

ease adoption and streamline deployment.

Motivations for a Dataflow Architecture

Computing applications and their associated operations require both computation and communication. In traditional

core-based architectures, the computation is programmed as required. However, the communications are managed by

the hardware and limited primarily to cache and memory transfers. This lack of ability to manage how data flows from one

intermediary calculation to the next can result in excessive data transfers and poor hardware utilization. 

references:

https://sambanova.ai/hubfs/23945802/SambaNova_Accelerated-Computing-with-a-Reconfigurable-Dataflow-Architecture_Whitepaper_English-1.pdf

What are the characteristics required for high and effective data flow processing?

Native dataflow — Commonly occurring operators in machine-learning frameworks and DSLs can be described in terms

of parallel patterns that capture parallelizable computation on both dense and sparse data collections along with

corresponding memory access patterns. This enables exploitation and high utilization of the underlying platform while

allowing a diverse set of models to be easily written in any framework of choice. 

Support for terabyte-sized models — A key trend in deep-learning model development uses increasingly large model

sizes to gain higher accuracy and deliver more sophisticated functionality. For example, leveraging billions of datapoints (referred to as parameters) enables more accurate Natural Language Generation. In the life sciences field,

analyzing tissue samples requires the processing of large, high-resolution images to identify subtle features. Providing

much larger on-chip and off-chip memory stores than those that are available on core-based architectures will

accelerate deep-learning innovation.

Efficient processing of sparse data and graph-based networks — Recommender systems, friend-of-friends problems,

knowledge graphs, some life-science domains and more involve large sparse data structures that consist of mostly zero

values. Moving around and processing large, mostly empty matrices is inefficient and degrades performance. A nextgeneration architecture must intelligently avoid unnecessary processing.

Flexible model mapping — Currently, data and model parallel techniques are used to scale workloads across the

infrastructure. However, the programming cost and complexity are often prohibiting factors for new deep-learning

approaches. A new architecture should automatically enable scaling across infrastructure without this added

development and orchestration complexity and avoid the need for model developers to become experts in system

architecture and parallel computing.

Incorporate SQL and other pre-/post data processing — As deep learning models grow and incorporate a wider variety

of data types, the dependency on pre-processing and post-processing of data becomes dominant. Additionally, the

time lag and cost of ETL operations impact real-time system goals. A new architecture should allow the unification of

these processing tasks on a single platform.

references:

https://sambanova.ai/hubfs/23945802/SambaNova_Accelerated-Computing-with-a-Reconfigurable-Dataflow-Architecture_Whitepaper_English-1.pdf

What is the formula for finding the number of back propagations?

To determine how many times the weights get updated during backpropagation, you can use the formula:

Weight updates = (Number of batches per epoch) × (Number of epochs)

Let's say we have a dataset of 1000 rows, and when a batch size of 10 and epoch of 10 is applied to this data, how many times do the weights get updated during the back propagation. 

Dataset size = 1000 rows

Batch size = 10

Epochs = 10

Step-by-step:

Batches per epoch = 1000 / 10 = 100

Epochs = 10

Total weight updates = 100 batches/epoch × 10 epochs = 1000

How to extract Tables from document

How to Extract Table Content from Documents

If you see a table in a document, you are normally not looking at something like an embedded Excel or other identifiable object. It usually is just normal, standard text, formatted to appear as tabular data.

Extracting tabular data from such a page area therefore means that you must find a way to identify the table area (i.e. its boundary box), then (1) graphically indicate table and column borders, and (2) then extract text based on this information.

This can be a very complex task, depending on details like the presence or absence of lines, rectangles or other supporting vector graphics.

Method Page.find_tables() does all that for you, with a high table detection precision. Its great advantage is that there are no external library dependencies, nor the need to employ artificial intelligence or machine learning technologies. It also provides an integrated interface to the well-known Python package for data analysis pandas.

Sample tables for analysis are given below 

https://github.com/pymupdf/PyMuPDF-Utilities/tree/master/table-analysis

https://github.com/pymupdf/PyMuPDF-Utilities/blob/master/table-analysis/find_tables.ipynb

Below is given sample code, which works 

from google.colab import drive

drive.mount('/content/drive')

file_path = '/content/drive/My Drive/temp/sample_pdfs/input1.pdf' 

doc = fitz.open(file_path)

page = doc[0]

tabs = page.find_tables()  # detect the tables

for i,tab in enumerate(tabs):  # iterate over all tables

    for cell in tab.header.cells:

        page.draw_rect(cell,color=fitz.pdfcolor["red"],width=0.3)

    page.draw_rect(tab.bbox,color=fitz.pdfcolor["green"])

    print(f"Table {i} column names: {tab.header.names}, external: {tab.header.external}")

    

show_image(page, f"Table & Header BBoxes")

To get the actual content, the code is like below 

# choose the second table for conversion to a DataFrame

tab = tabs[0]

df = tab.to_pandas()

# show the DataFrame

df

references:

https://pymupdf.readthedocs.io/en/latest/recipes-text.html#how-to-extract-table-content-from-documents

https://github.com/pymupdf/PyMuPDF-Utilities/tree/master/table-analysis

What is Leiden Algorithm

The Leiden algorithm is a hierarchical clustering algorithm used for detecting communities (clusters) within networks by optimizing modularity, which measures the quality of the network's division into clusters. It builds upon the Louvain algorithm but addresses its limitations, particularly the tendency to produce poorly connected communities. Leiden achieves this by introducing a refinement phase that allows for the dynamic reassignment of nodes and the breaking down of communities into smaller, well-connected ones. 

Here's a more detailed breakdown:

Key aspects of the Leiden algorithm:

Hierarchical Clustering:

It operates by iteratively merging or splitting communities to find the best possible structure. 

Modularity Optimization:

The algorithm aims to maximize the modularity of the network, which indicates how densely connected nodes within a community are, compared to a random network. 

Addressing Louvain Limitations:

Leiden improves upon Louvain by introducing a refinement phase that ensures communities are well-connected and that no nodes are left in disconnected communities. 

Addressing Louvain Limitations:

Leiden improves upon Louvain by introducing a refinement phase that ensures communities are well-connected and that no nodes are left in disconnected communities. 

Refinement Phase:

This phase allows nodes to dynamically reassign themselves across overlapping clusters, leading to more accurate and meaningful community structures. 

Aggregate Network:

The algorithm creates an aggregate network based on the refined partition, using the non-refined partition as an initial partition for the aggregate network. 

Iterative Process:

The process of local moving and refinement is repeated until no further improvements can be made, resulting in a stable and well-defined community structure. 

Microsoft's Graph Rag approach

There are different implementations of it, here we focus on Microsoft’s approach. It can be broken down into two main steps: Graph Creation (i.e. Indexing) and Querying (of which there are three possibilities: Local Search, Global Search and Drift Search).

real-world example to walk you through Graph Creation, Local Search, and Global Search. So without further ado, let’s index and query the book Penitencia by Pablo Rivero using GraphRAG.

The GraphRAG documentation walks you through project set-up. Once you initialize your workspace, you’ll find a configuration file (settings.yaml) in the ragtest directory.

To create the graph, run:

graphrag index --root ./ragtest

This triggers two key actions, Entity Extraction from Source Document(s) and Graph Partitioning into Communities, as defined in modules of the workflows directory of the GraphRAG project.

Entity Extraction

1.. In the create_base_text_units module, documents (in our case, the book Penitencia) are split into smaller chunks of N tokens.

In create_final_documents, a lookup table is created to map documents to their associated text units. Each row represents a document and since we are only working with one document, there is only one row.

In extract_graph, each chunk is analyzed using an LLM (from OpenAI) to extract entities and relationships guided by this prompt.

https://github.com/microsoft/graphrag/blob/main/graphrag/prompts/index/extract_graph.py

It is given as below 

GRAPH_EXTRACTION_PROMPT = """

-Goal-

Given a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities.

 

-Steps-

1. Identify all entities. For each identified entity, extract the following information:

- entity_name: Name of the entity, capitalized

- entity_type: One of the following types: [{entity_types}]

- entity_description: Comprehensive description of the entity's attributes and activities

Format each entity as ("entity"{tuple_delimiter}<entity_name>{tuple_delimiter}<entity_type>{tuple_delimiter}<entity_description>)

 

2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.

For each pair of related entities, extract the following information:

- source_entity: name of the source entity, as identified in step 1

- target_entity: name of the target entity, as identified in step 1

- relationship_description: explanation as to why you think the source entity and the target entity are related to each other

- relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity

 Format each relationship as ("relationship"{tuple_delimiter}<source_entity>{tuple_delimiter}<target_entity>{tuple_delimiter}<relationship_description>{tuple_delimiter}<relationship_strength>)

 

3. Return output in English as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter.

 

4. When finished, output {completion_delimiter}

 

######################

-Examples-

######################

Example 1:

Entity_types: ORGANIZATION,PERSON

Text:

The Verdantis's Central Institution is scheduled to meet on Monday and Thursday, with the institution planning to release its latest policy decision on Thursday at 1:30 p.m. PDT, followed by a press conference where Central Institution Chair Martin Smith will take questions. Investors expect the Market Strategy Committee to hold its benchmark interest rate steady in a range of 3.5%-3.75%.

######################

Output:

("entity"{tuple_delimiter}CENTRAL INSTITUTION{tuple_delimiter}ORGANIZATION{tuple_delimiter}The Central Institution is the Federal Reserve of Verdantis, which is setting interest rates on Monday and Thursday)

{record_delimiter}

("entity"{tuple_delimiter}MARTIN SMITH{tuple_delimiter}PERSON{tuple_delimiter}Martin Smith is the chair of the Central Institution)

{record_delimiter}

("entity"{tuple_delimiter}MARKET STRATEGY COMMITTEE{tuple_delimiter}ORGANIZATION{tuple_delimiter}The Central Institution committee makes key decisions about interest rates and the growth of Verdantis's money supply)

{record_delimiter}

("relationship"{tuple_delimiter}MARTIN SMITH{tuple_delimiter}CENTRAL INSTITUTION{tuple_delimiter}Martin Smith is the Chair of the Central Institution and will answer questions at a press conference{tuple_delimiter}9)

{completion_delimiter}

######################

Example 2:

Entity_types: ORGANIZATION

Text:

TechGlobal's (TG) stock skyrocketed in its opening day on the Global Exchange Thursday. But IPO experts warn that the semiconductor corporation's debut on the public markets isn't indicative of how other newly listed companies may perform.

TechGlobal, a formerly public company, was taken private by Vision Holdings in 2014. The well-established chip designer says it powers 85% of premium smartphones.

######################

Output:

("entity"{tuple_delimiter}TECHGLOBAL{tuple_delimiter}ORGANIZATION{tuple_delimiter}TechGlobal is a stock now listed on the Global Exchange which powers 85% of premium smartphones)

{record_delimiter}

("entity"{tuple_delimiter}VISION HOLDINGS{tuple_delimiter}ORGANIZATION{tuple_delimiter}Vision Holdings is a firm that previously owned TechGlobal)

{record_delimiter}

("relationship"{tuple_delimiter}TECHGLOBAL{tuple_delimiter}VISION HOLDINGS{tuple_delimiter}Vision Holdings formerly owned TechGlobal from 2014 until present{tuple_delimiter}5)

{completion_delimiter}

######################

Example 3:

Entity_types: ORGANIZATION,GEO,PERSON

Text:

Five Aurelians jailed for 8 years in Firuzabad and widely regarded as hostages are on their way home to Aurelia.

The swap orchestrated by Quintara was finalized when $8bn of Firuzi funds were transferred to financial institutions in Krohaara, the capital of Quintara.

The exchange initiated in Firuzabad's capital, Tiruzia, led to the four men and one woman, who are also Firuzi nationals, boarding a chartered flight to Krohaara.

They were welcomed by senior Aurelian officials and are now on their way to Aurelia's capital, Cashion.

The Aurelians include 39-year-old businessman Samuel Namara, who has been held in Tiruzia's Alhamia Prison, as well as journalist Durke Bataglani, 59, and environmentalist Meggie Tazbah, 53, who also holds Bratinas nationality.

######################

Output:

("entity"{tuple_delimiter}FIRUZABAD{tuple_delimiter}GEO{tuple_delimiter}Firuzabad held Aurelians as hostages)

{record_delimiter}

("entity"{tuple_delimiter}AURELIA{tuple_delimiter}GEO{tuple_delimiter}Country seeking to release hostages)

{record_delimiter}

("entity"{tuple_delimiter}QUINTARA{tuple_delimiter}GEO{tuple_delimiter}Country that negotiated a swap of money in exchange for hostages)

{record_delimiter}

{record_delimiter}

("entity"{tuple_delimiter}TIRUZIA{tuple_delimiter}GEO{tuple_delimiter}Capital of Firuzabad where the Aurelians were being held)

{record_delimiter}

("entity"{tuple_delimiter}KROHAARA{tuple_delimiter}GEO{tuple_delimiter}Capital city in Quintara)

{record_delimiter}

("entity"{tuple_delimiter}CASHION{tuple_delimiter}GEO{tuple_delimiter}Capital city in Aurelia)

{record_delimiter}

("entity"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}PERSON{tuple_delimiter}Aurelian who spent time in Tiruzia's Alhamia Prison)

{record_delimiter}

("entity"{tuple_delimiter}ALHAMIA PRISON{tuple_delimiter}GEO{tuple_delimiter}Prison in Tiruzia)

{record_delimiter}

("entity"{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}PERSON{tuple_delimiter}Aurelian journalist who was held hostage)

{record_delimiter}

("entity"{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}PERSON{tuple_delimiter}Bratinas national and environmentalist who was held hostage)

{record_delimiter}

("relationship"{tuple_delimiter}FIRUZABAD{tuple_delimiter}AURELIA{tuple_delimiter}Firuzabad negotiated a hostage exchange with Aurelia{tuple_delimiter}2)

{record_delimiter}

("relationship"{tuple_delimiter}QUINTARA{tuple_delimiter}AURELIA{tuple_delimiter}Quintara brokered the hostage exchange between Firuzabad and Aurelia{tuple_delimiter}2)

{record_delimiter}

("relationship"{tuple_delimiter}QUINTARA{tuple_delimiter}FIRUZABAD{tuple_delimiter}Quintara brokered the hostage exchange between Firuzabad and Aurelia{tuple_delimiter}2)

{record_delimiter}

("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}ALHAMIA PRISON{tuple_delimiter}Samuel Namara was a prisoner at Alhamia prison{tuple_delimiter}8)

{record_delimiter}

("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}Samuel Namara and Meggie Tazbah were exchanged in the same hostage release{tuple_delimiter}2)

{record_delimiter}

("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}Samuel Namara and Durke Bataglani were exchanged in the same hostage release{tuple_delimiter}2)

{record_delimiter}

("relationship"{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}Meggie Tazbah and Durke Bataglani were exchanged in the same hostage release{tuple_delimiter}2)

{record_delimiter}

("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}FIRUZABAD{tuple_delimiter}Samuel Namara was a hostage in Firuzabad{tuple_delimiter}2)

{record_delimiter}

("relationship"{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}FIRUZABAD{tuple_delimiter}Meggie Tazbah was a hostage in Firuzabad{tuple_delimiter}2)

{record_delimiter}

("relationship"{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}FIRUZABAD{tuple_delimiter}Durke Bataglani was a hostage in Firuzabad{tuple_delimiter}2)

{completion_delimiter}

######################

-Real Data-

######################

Entity_types: {entity_types}

Text: {input_text}

######################

Output:"""

CONTINUE_PROMPT = "MANY entities and relationships were missed in the last extraction. Remember to ONLY emit entities that match any of the previously extracted types. Add them below using the same format:\n"

LOOP_PROMPT = "It appears some entities and relationships may have still been missed. Answer Y if there are still entities or relationships that need to be added, or N if there are none. Please answer with a single letter Y or N.\n"

During this process, duplicate entities and relationships may appear. For example, the main character Jon is mentioned in 82 different text chunks, so he was extracted 82 times — once for each chunk.

An attempt at deduplication is made by grouping together entities based on their title and type, and grouping together relationships based on their source and target nodes. Then, the LLM is prompted to write a detailed description for each unique entity and unique relationship by analyzing the shorter descriptions from all occurrences 

4. In finalize_graph, the NetworkX library is used to represent the entities and relationships as the nodes and edges of a graph, including structural information like node degree.

Graph Partitioning into Communities

5. In create_communities, the graph is partitioned into communities using the Leiden algorithm, a hierarchical clustering algorithm.

References:

https://microsoft.github.io/graphrag/get_started/

Does LLMsherpa use an API to do the parsing? How does it work?

The llmsherpa LayoutPDFReader itself primarily focuses on structure extraction from PDFs, and it does not directly use an LLM for that core task.

Here's a more detailed explanation:

What LayoutPDFReader Does: It's designed to parse PDFs and understand their layout, identifying elements like sections, paragraphs, tables, and lists. This is crucial for preparing PDF content for use with LLMs. It aims to provide a more structured representation of the PDF content than a simple text extraction.

How it Works: LayoutPDFReader uses an API (which may be hosted by llmsherpa) to analyze the PDF and return a structured representation. This process involves parsing the PDF's internal structure.

LLMs in the Broader Context: While LayoutPDFReader doesn't use an LLM for its primary parsing, the output from LayoutPDFReader is intended to be used with LLMs. The structured data it provides makes it much easier to feed PDF content into an LLM for tasks like:

Retrieval Augmented Generation (RAG): Where you retrieve relevant chunks of text from a PDF (processed by LayoutPDFReader) and provide them to an LLM to answer a question.

Summarization: Where you use an LLM to summarize sections of a PDF identified by LayoutPDFReader.

Regarding API Keys:

LayoutPDFReader often interacts with an API to perform the PDF parsing. Therefore, you might need to use an API. The documentation mentions the need for an LLMSherpa API URL.

In summary, LayoutPDFReader is a tool that helps in intelligently extracting information from PDFs, and this structured information is then very useful for LLMs.

References 

OpenAI

What are reasons for Slow Execution of GridSearchCV?

# RandomForestClassifier with GridSearchCV for hyperparameter tuning

param_grid = {

    "n_estimators": [50, 100, 200],  # Number of trees

    "max_depth": [None, 10, 20, 30],  # Maximum depth of the trees

    "min_samples_split": [2, 5, 10],  # Minimum samples required to split an internal node

    "min_samples_leaf": [1, 2, 4],  # Minimum samples required to be at a leaf node

    "criterion": ["gini", "entropy"],

}

grid_search_rf = GridSearchCV(

    RandomForestClassifier(random_state=1),  # Keep random_state for reproducibility

    param_grid,

    cv=5,  # 5-fold cross-validation

    scoring="accuracy",  # Optimize for accuracy

)

grid_search_rf.fit(X_train, y_train)

best_rf_classifier = grid_search_rf.best_estimator_ # Get the best model

Exhaustive Search: GridSearchCV tries every combination of parameters in your param_grid.  The more combinations, the longer it takes.

Cross-Validation:  The cv parameter (in your case, 5) means that for each parameter combination, the model is trained and evaluated 5 times. This multiplies the training time.

Model Complexity: Random Forest itself can be computationally intensive, especially with more trees (n_estimators) and deeper trees (max_depth).

Data Size:  The larger your X_train and y_train, the longer each model training and evaluation takes.

Number of Parameters: Each parameter added to the grid increases the search space exponentially

Strategies to Reduce Time During Development:

Reduce the Parameter Grid Size:

Fewer Values: Instead of [50, 100, 200], try [50, 100] for n_estimators.

Coarser Grid:  Instead of [None, 10, 20, 30], try [None, 20] for max_depth.

Fewer Parameters:  Start with a smaller subset of parameters. For example, initially, tune only n_estimators and max_depth, and fix the others to their default values.  Later, add more parameters to the grid.

Example:

param_grid = {

    "n_estimators": [50, 100],

    "max_depth": [10, 20],

    # "min_samples_split": [2, 5],  # Removed some values

    # "min_samples_leaf": [1, 2],

    "criterion": ["gini"],  # Fixed to 'gini'

}

Reduce Cross-Validation Folds:

Use cv=3 instead of cv=5 during development.  This reduces the number of training/evaluation rounds.  You can increase it to 5 (or 10) for the final run when you're confident in your parameter ranges.

grid_search_rf = GridSearchCV(..., cv=3, ...)

Use a Smaller Subset of Data:

During initial development and testing, train GridSearchCV on a smaller sample of your training data.  For example, use the first 1000 rows.  Once you've found a good range of parameters, train on the full dataset.

X_train_small, _, y_train_small, _ = train_test_split(X_train, y_train, train_size=1000, random_state=42)

grid_search_rf.fit(X_train_small, y_train_small)

Be cautious about using a too small subset, as it might not be representative of the full dataset, and the optimal hyperparameters might be different.

Consider RandomizedSearchCV:

If GridSearchCV is still too slow, consider RandomizedSearchCV.  Instead of trying all combinations, it samples a fixed number of parameter combinations.  This is often much faster, especially with a large parameter space, and can still find good (though not necessarily optimal) hyperparameters.

from sklearn.model_selection import RandomizedSearchCV

param_distributions = {  # Use param_distributions, not param_grid

    "n_estimators": [50, 100, 200, 300],

    "max_depth": [None, 10, 20, 30, 40],

    "min_samples_split": [2, 5, 10, 15],

    "min_samples_leaf": [1, 2, 4, 6],

    "criterion": ["gini", "entropy"],

}

random_search_rf = RandomizedSearchCV(

    RandomForestClassifier(random_state=1),

    param_distributions,  # Use param_distributions

    n_iter=10,  # Number of random combinations to try

    cv=3,

    scoring="accuracy",

    random_state=42,  # Important for reproducibility

)

random_search_rf.fit(X_train, y_train)

best_rf_classifier = random_search_rf.best_estimator_

n_iter controls how many random combinations are tried.  A smaller n_iter will be faster.

Use Parallel Processing (if available):

If your machine has multiple CPU cores, use the n_jobs parameter in GridSearchCV and RandomizedSearchCV.  Setting n_jobs=-1 will use all available cores.  This can significantly speed up the process, especially with larger datasets and complex models.

grid_search_rf = GridSearchCV(..., n_jobs=-1, ...)

However, be aware that parallel processing can increase memory consumption.

Early Stopping (Not Directly Applicable to RandomForest in GridSearchCV):

Some models (like Gradient Boosting) have built-in early stopping mechanisms that can halt training when performance on a validation set stops improving.  Random Forest itself doesn't have this, and GridSearchCV doesn't directly provide early stopping.

Summary of Recommendations:

For faster development:

Start with a smaller param_grid.

Use a lower cv (e.g., 3).

Consider training on a smaller data subset initially.

Explore RandomizedSearchCV for a faster, though potentially less exhaustive, search.

Use n_jobs=-1 to leverage multiple CPU cores.

Remember to increase cv and use the full dataset for your final model training and evaluation to ensure you get the best possible performance.