Develop Machine Learning Models for Various NFL Analytics Needs:

[zepor]

    ○ Time: 6 weeks
    ○ Tools Required: Scikit-learn, TensorFlow, PyTorch (within Azure AI Studio or Microsoft Fabric)
    ○ Steps:
        1. Define model requirements and objectives.
            □ Utilize historical NFL data to identify key metrics and outcomes for predictive modeling.
        2. Develop and train machine learning models for different analytics needs (e.g., player performance prediction, game outcome prediction).
            □ Models can be built using Scikit-learn for simpler algorithms and TensorFlow/PyTorch for neural networks and deep learning.
        3. Perform feature engineering and selection to improve model accuracy.
        4. Regularly review and iterate on models to ensure they meet the set objectives.
        5. Store model credentials and configurations in GitHub Secrets.
            □ Secrets Needed: AZURE_ML_WORKSPACE_CONNECTION, AZURE_ML_MODEL_REGISTRY
    ○ Documentation:
        § Detailed model architecture and design documents.
        § Jupyter notebooks or Python scripts for model training and evaluation.
        § Data preprocessing and feature engineering steps.
    ○ Additional Details: Use cross-validation as the primary validation technique.
    ○ Major Milestone: Machine Learning models developed and trained.
    ○ GitHub Issue:

Develop Machine Learning Models for NFL Analytics

Description: Develop and train machine learning models using historical NFL data. Tasks:

• Define model requirements and objectives.
• Develop and train models using Scikit-learn, TensorFlow, and PyTorch.
• Perform feature engineering and selection.
• Store model credentials in GitHub Secrets: AZURE_ML_WORKSPACE_CONNECTION, AZURE_ML_MODEL_REGISTRY. Milestone: Machine Learning models developed and trained.

[codeautopilot[bot]]

Potential solution

The task involves developing machine learning models for NFL analytics using Scikit-learn, TensorFlow, and PyTorch. The solution requires defining model requirements, performing feature engineering, training models, and storing credentials securely. The proposed changes to the files will ensure that the necessary modules and functions are implemented and accessible throughout the project.

How to implement

File: backend-container/src/utils/init.py

Update the __init__.py file to include the feature_engineering, cross_validation, and github_secrets modules.

from . import feature_engineering
from . import cross_validation
from . import github_secrets

File: backend-container/src/models/init.py

Update the __init__.py file to include the ml_models module.

from .draft_info import DraftInfo
from .franchise_info import FranchiseInfo
from .game_stat_team_info import GameStatsTeamInfo
from .game_stat_team_summary_info import GameStatTeamSummaryInfo
from .play_by_play_game_stats_team_info import PlayByPlayGameStatsTeamInfo
from .metadata_info import MetaDataInfo
from .play_by_play_info import PlayByPlayInfo
from .play_pulse import PulsePlay
from .play_stats import PlayerStats
from .player_DCI_info import PlayerDCIinfo
from .season_stat_oppo_info import SeasonStatOppo
from .season_stat_player_info import SeasonStatPlayer
from .season_stat_team_info import SeasonStatTeam
from .standings_info import StandingsInfo
from .team_info import TeamInfo
from .transactions_info import TransactionInfo
from .venue_info import VenueInfo
from .league_info import LeagueInfo
from .game_info import GameInfo
from .boxscore_info import BoxscoreInfo
from .leaguehierarchy import LeagueHierarchy
from .seasons import SeasonInfo
from .ml_models import MLModels  # Add this line to include the ml_models module

File: backend-container/src/utils/feature_engineering.py

Implement functions for feature engineering and selection.

import pandas as pd
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.feature_selection import SelectKBest, f_classif

def handle_missing_values(df, strategy='mean'):
    imputer = SimpleImputer(strategy=strategy)
    return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)

def encode_categorical_features(df, categorical_columns):
    encoder = OneHotEncoder(sparse=False, drop='first')
    encoded_features = encoder.fit_transform(df[categorical_columns])
    encoded_df = pd.DataFrame(encoded_features, columns=encoder.get_feature_names_out(categorical_columns))
    return df.drop(columns=categorical_columns).join(encoded_df)

def scale_numerical_features(df, numerical_columns):
    scaler = StandardScaler()
    df[numerical_columns] = scaler.fit_transform(df[numerical_columns])
    return df

def select_best_features(X, y, k=10):
    selector = SelectKBest(score_func=f_classif, k=k)
    X_new = selector.fit_transform(X, y)
    selected_features = X.columns[selector.get_support()]
    return pd.DataFrame(X_new, columns=selected_features)

File: backend-container/src/utils/github_secrets.py

Implement functions to store and retrieve GitHub secrets.

from github import Github
import os

def get_github_client():
    token = os.getenv('GITHUB_TOKEN')
    if not token:
        raise ValueError("GITHUB_TOKEN environment variable not set")
    return Github(token)

def store_secret(repo_name, secret_name, secret_value):
    client = get_github_client()
    repo = client.get_repo(repo_name)
    repo.create_secret(secret_name, secret_value)
    print(f"Secret {secret_name} stored successfully in {repo_name}")

def retrieve_secret(repo_name, secret_name):
    client = get_github_client()
    repo = client.get_repo(repo_name)
    secret = repo.get_secret(secret_name)
    return secret

File: backend-container/src/utils/cross_validation.py

Implement cross-validation techniques for model validation.

from sklearn.model_selection import KFold, StratifiedKFold, TimeSeriesSplit, cross_val_score
import numpy as np

def k_fold_cross_validation(model, X, y, n_splits=5):
    kf = KFold(n_splits=n_splits)
    scores = cross_val_score(model, X, y, cv=kf)
    return np.mean(scores), np.std(scores)

def stratified_k_fold_cross_validation(model, X, y, n_splits=5):
    skf = StratifiedKFold(n_splits=n_splits)
    scores = cross_val_score(model, X, y, cv=skf)
    return np.mean(scores), np.std(scores)

def time_series_cross_validation(model, X, y, n_splits=5):
    tscv = TimeSeriesSplit(n_splits=n_splits)
    scores = cross_val_score(model, X, y, cv=tscv)
    return np.mean(scores), np.std(scores)

File: backend-container/docs/model_documentation.md

Create comprehensive documentation for the models.

# Model Documentation

## 1. Model Architecture and Design
### 1.1 Types of Models
- Linear Regression
- Neural Networks (TensorFlow, PyTorch)

### 1.2 Model Architecture
- **Linear Regression**: Simple linear model with input features and a single output.
- **Neural Networks**: 
  - Input Layer: [Describe input features]
  - Hidden Layers: [Number of layers, types of layers, activation functions]
  - Output Layer: [Describe output]

### 1.3 Rationale for Model Selection
- [Explain why specific models were chosen for different analytics needs]

## 2. Data Preprocessing
### 2.1 Data Cleaning
- [Describe steps taken to clean the data]

### 2.2 Data Normalization
- [Explain normalization techniques used]

### 2.3 Handling Missing Values
- [Describe methods used to handle missing data]

### 2.4 Data Augmentation
- [Detail any data augmentation techniques applied]

## 3. Feature Engineering
### 3.1 Feature Selection
- [Describe how features were selected]

### 3.2 Feature Creation
- [Explain the process of creating new features]

### 3.3 Feature Importance
- [Discuss how feature importance was determined]

### 3.4 Dimensionality Reduction
- [Describe any dimensionality reduction techniques used]

## 4. Model Training and Evaluation
### 4.1 Training Process
- [Outline the training process]

### 4.2 Evaluation Metrics
- [Discuss evaluation metrics used]

### 4.3 Cross-Validation Results
- [Include cross-validation results]

## 5. Model Iteration and Improvement
### 5.1 Iterative Process
- [Explain the iterative process of refining models]

### 5.2 Challenges and Solutions
- [Discuss challenges faced and solutions implemented]

### 5.3 Future Improvements
- [Provide insights into future improvements]

## 6. Configuration and Deployment
### 6.1 Storing Credentials
- [Detail how credentials are stored using GitHub Secrets]

### 6.2 Deployment Process
- [Explain the deployment process within Azure AI Studio or Microsoft Fabric]

### 6.3 Deployment Scripts
- [Include any scripts or commands used for deployment]

File: backend-container/src/models/ml_models.py

Implement functions to define model requirements, develop and train models, and perform feature engineering.

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import tensorflow as tf
import torch
import torch.nn as nn
import torch.optim as optim

def define_model_requirements():
    requirements = {
        'player_performance': {
            'description': 'Predict player performance based on historical data.',
            'metrics': ['yards', 'touchdowns', 'interceptions']
        },
        'game_outcome': {
            'description': 'Predict game outcomes based on team statistics.',
            'metrics': ['win', 'loss']
        }
    }
    return requirements

def develop_and_train_sklearn_model(X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    print(f'Scikit-learn Model Accuracy: {accuracy}')
    return model

def develop_and_train_tensorflow_model(X, y):
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(128, activation='relu', input_shape=(X.shape[1],)),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    model.fit(X, y, epochs=10, batch_size=32, validation_split=0.2)
    return model

def develop_and_train_pytorch_model(X, y):
    class SimpleNN(nn.Module):
        def __init__(self):
            super(SimpleNN, self).__init__()
            self.fc1 = nn.Linear(X.shape[1], 128)
            self.fc2 = nn.Linear(128, 64)
            self.fc3 = nn.Linear(64, 1)

        def forward(self, x):
            x = torch.relu(self.fc1(x))
            x = torch.relu(self.fc2(x))
            x = torch.sigmoid(self.fc3(x))
            return x

    model = SimpleNN()
    criterion = nn.BCELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    X_train, y_train = torch.tensor(X_train, dtype=torch.float32), torch.tensor(y_train, dtype=torch.float32)
    X_test, y_test = torch.tensor(X_test, dtype=torch.float32), torch.tensor(y_test, dtype=torch.float32)

    for epoch in range(10):
        model.train()
        optimizer.zero_grad()
        outputs = model(X_train)
        loss = criterion(outputs.squeeze(), y_train)
        loss.backward()
        optimizer.step()
        print(f'Epoch {epoch+1}, Loss: {loss.item()}')

    model.eval()
    with torch.no_grad():
        outputs = model(X_test).squeeze()
        predicted = (outputs > 0.5).float()
        accuracy = (predicted == y_test).float().mean()
        print(f'PyTorch Model Accuracy: {accuracy.item()}')

    return model

def perform_feature_engineering(data):
    data['feature1'] = data['raw_feature1'] / data['raw_feature2']
    data['feature2'] = data['raw_feature3'] * data['raw_feature4']
    selected_features = ['feature1', 'feature2', 'raw_feature5']
    return data[selected_features]

if __name__ == "__main__":
    data = pd.read_csv('nfl_data.csv')
    X = perform_feature_engineering(data)
    y = data['target']

    requirements = define_model_requirements()
    print(requirements)

    sklearn_model = develop_and_train_sklearn_model(X, y)
    tensorflow_model = develop_and_train_tensorflow_model(X, y)
    pytorch_model = develop_and_train_pytorch_model(X, y)

Click here to create a Pull Request with the proposed solution

Files used for this task:

Changes on backend-container/src/utils/__init__.py

To address the task for the `backend-container/src/utils/__init__.py` file, you need to update it to include the `feature_engineering`, `cross_validation`, and `github_secrets` modules. This will ensure that these modules are accessible when the `utils` package is imported. Here is the updated content for the `__init__.py` file: ```python from . import feature_engineering from . import cross_validation from . import github_secrets ``` This change will allow you to import these modules using the `utils` package, facilitating their use in other parts of the project.

Changes on backend-container/src/models/__init__.py

To address the task for the `backend-container/src/models/__init__.py` file, you need to update the `__init__.py` file to include the `ml_models` module. This will ensure that the `ml_models` module is properly imported and accessible within the `models` package. Here is the updated content for the `__init__.py` file: ```python from .draft_info import DraftInfo from .franchise_info import FranchiseInfo from .game_stat_team_info import GameStatsTeamInfo from .game_stat_team_summary_info import GameStatTeamSummaryInfo from .play_by_play_game_stats_team_info import PlayByPlayGameStatsTeamInfo from .metadata_info import MetaDataInfo from .play_by_play_info import PlayByPlayInfo from .play_pulse import PulsePlay from .play_stats import PlayerStats from .player_DCI_info import PlayerDCIinfo from .season_stat_oppo_info import SeasonStatOppo from .season_stat_player_info import SeasonStatPlayer from .season_stat_team_info import SeasonStatTeam from .standings_info import StandingsInfo from .team_info import TeamInfo from .transactions_info import TransactionInfo from .venue_info import VenueInfo from .league_info import LeagueInfo from .game_info import GameInfo from .boxscore_info import BoxscoreInfo from .leaguehierarchy import LeagueHierarchy from .seasons import SeasonInfo from .ml_models import MLModels # Add this line to include the ml_models module ``` This change ensures that the `MLModels` class or functions defined in the `ml_models.py` file are included in the `models` package and can be used throughout the project. If there are no further changes required for this file, this update should be sufficient.

Changes on backend-container/src/utils/feature_engineering.py

To address the task for the file `backend-container/src/utils/feature_engineering.py`, we need to implement functions that will handle feature engineering and selection. These functions will preprocess the data to enhance the performance of the machine learning models. Below is a concrete plan and a sample implementation to get started: ### Plan 1. **Define Feature Engineering Functions**: - Create functions to handle common feature engineering tasks such as handling missing values, encoding categorical variables, scaling numerical features, and creating new features. 2. **Define Feature Selection Functions**: - Implement functions to select the most relevant features using techniques like correlation analysis, feature importance from models, and recursive feature elimination. 3. **Ensure Modularity**: - Ensure that each function is modular and can be easily tested and reused. ### Sample Implementation ```python # backend-container/src/utils/feature_engineering.py import pandas as pd from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.impute import SimpleImputer from sklearn.feature_selection import SelectKBest, f_classif def handle_missing_values(df, strategy='mean'): """ Handle missing values in the DataFrame. Parameters: df (pd.DataFrame): The input DataFrame. strategy (str): The imputation strategy ('mean', 'median', 'most_frequent', 'constant'). Returns: pd.DataFrame: DataFrame with missing values handled. """ imputer = SimpleImputer(strategy=strategy) return pd.DataFrame(imputer.fit_transform(df), columns=df.columns) def encode_categorical_features(df, categorical_columns): """ Encode categorical features using OneHotEncoder. Parameters: df (pd.DataFrame): The input DataFrame. categorical_columns (list): List of categorical columns to encode. Returns: pd.DataFrame: DataFrame with categorical features encoded. """ encoder = OneHotEncoder(sparse=False, drop='first') encoded_features = encoder.fit_transform(df[categorical_columns]) encoded_df = pd.DataFrame(encoded_features, columns=encoder.get_feature_names_out(categorical_columns)) return df.drop(columns=categorical_columns).join(encoded_df) def scale_numerical_features(df, numerical_columns): """ Scale numerical features using StandardScaler. Parameters: df (pd.DataFrame): The input DataFrame. numerical_columns (list): List of numerical columns to scale. Returns: pd.DataFrame: DataFrame with numerical features scaled. """ scaler = StandardScaler() df[numerical_columns] = scaler.fit_transform(df[numerical_columns]) return df def select_best_features(X, y, k=10): """ Select the best features using SelectKBest. Parameters: X (pd.DataFrame): The input features. y (pd.Series): The target variable. k (int): Number of top features to select. Returns: pd.DataFrame: DataFrame with the best features selected. """ selector = SelectKBest(score_func=f_classif, k=k) X_new = selector.fit_transform(X, y) selected_features = X.columns[selector.get_support()] return pd.DataFrame(X_new, columns=selected_features) # Example usage: # df = pd.read_csv('nfl_data.csv') # df = handle_missing_values(df) # df = encode_categorical_features(df, categorical_columns=['team', 'position']) # df = scale_numerical_features(df, numerical_columns=['age', 'height', 'weight']) # X = df.drop(columns=['target']) # y = df['target'] # X_best = select_best_features(X, y) ``` ### Explanation 1. **handle_missing_values**: This function handles missing values in the DataFrame using a specified strategy. 2. **encode_categorical_features**: This function encodes categorical features using one-hot encoding. 3. **scale_numerical_features**: This function scales numerical features using standard scaling. 4. **select_best_features**: This function selects the best features based on the ANOVA F-value between the feature and the target variable. These functions provide a solid foundation for feature engineering and selection, which are crucial steps in developing robust machine learning models.

Changes on backend-container/src/utils/github_secrets.py

To address the task for the `backend-container/src/utils/github_secrets.py` file, you need to implement functions that can store and retrieve secrets such as `AZURE_ML_WORKSPACE_CONNECTION` and `AZURE_ML_MODEL_REGISTRY`. These secrets are essential for securely connecting to Azure Machine Learning services. Here is a concrete proposal for implementing these functions: 1. **Install Required Libraries**: Ensure you have the `PyGithub` library installed to interact with GitHub secrets. ```bash pip install PyGithub ``` 2. **Implement Functions**: Create functions to store and retrieve secrets using the GitHub API. ### Implementation ```python from github import Github import os def get_github_client(): """ Initialize and return a GitHub client using a personal access token. """ token = os.getenv('GITHUB_TOKEN') if not token: raise ValueError("GITHUB_TOKEN environment variable not set") return Github(token) def store_secret(repo_name, secret_name, secret_value): """ Store a secret in the specified GitHub repository. :param repo_name: str, the name of the repository (e.g., 'user/repo') :param secret_name: str, the name of the secret to store :param secret_value: str, the value of the secret to store """ client = get_github_client() repo = client.get_repo(repo_name) repo.create_secret(secret_name, secret_value) print(f"Secret {secret_name} stored successfully in {repo_name}") def retrieve_secret(repo_name, secret_name): """ Retrieve a secret from the specified GitHub repository. :param repo_name: str, the name of the repository (e.g., 'user/repo') :param secret_name: str, the name of the secret to retrieve :return: str, the value of the secret """ client = get_github_client() repo = client.get_repo(repo_name) secret = repo.get_secret(secret_name) return secret # Example usage: # store_secret('user/repo', 'AZURE_ML_WORKSPACE_CONNECTION', 'your_connection_string') # secret_value = retrieve_secret('user/repo', 'AZURE_ML_WORKSPACE_CONNECTION') # print(secret_value) ``` ### Explanation 1. **get_github_client**: Initializes a GitHub client using a personal access token stored in the environment variable `GITHUB_TOKEN`. 2. **store_secret**: Stores a secret in the specified GitHub repository. It takes the repository name, secret name, and secret value as parameters. 3. **retrieve_secret**: Retrieves a secret from the specified GitHub repository. It takes the repository name and secret name as parameters and returns the secret value. ### Environment Setup Ensure you have a GitHub personal access token with the necessary permissions to manage secrets in your repository. Set this token as an environment variable: ```bash export GITHUB_TOKEN='your_personal_access_token' ``` ### Notes - **Security**: Always handle secrets securely. Avoid hardcoding sensitive information in your code. - **Permissions**: Ensure the GitHub token has the necessary permissions to read and write secrets in the repository. This implementation will allow you to securely store and retrieve secrets needed for your machine learning models in GitHub.

Changes on backend-container/src/utils/cross_validation.py

To implement cross-validation techniques for validating the machine learning models in the `backend-container/src/utils/cross_validation.py` file, follow these steps: 1. **Import Necessary Libraries**: - Import the required libraries such as `sklearn.model_selection` for cross-validation techniques. 2. **Define Cross-Validation Functions**: - Implement functions for different cross-validation techniques such as K-Fold, Stratified K-Fold, and TimeSeriesSplit. 3. **Parameterize the Functions**: - Allow the functions to accept parameters such as the model, data, number of splits, and any other relevant parameters. 4. **Return Cross-Validation Results**: - Ensure the functions return relevant metrics such as accuracy, precision, recall, F1-score, etc. Here is a concrete implementation plan for the `cross_validation.py` file: ### Step-by-Step Implementation 1. **Import Libraries**: ```python from sklearn.model_selection import KFold, StratifiedKFold, TimeSeriesSplit, cross_val_score import numpy as np ``` 2. **Define Cross-Validation Functions**: ```python def k_fold_cross_validation(model, X, y, n_splits=5): kf = KFold(n_splits=n_splits) scores = cross_val_score(model, X, y, cv=kf) return np.mean(scores), np.std(scores) def stratified_k_fold_cross_validation(model, X, y, n_splits=5): skf = StratifiedKFold(n_splits=n_splits) scores = cross_val_score(model, X, y, cv=skf) return np.mean(scores), np.std(scores) def time_series_cross_validation(model, X, y, n_splits=5): tscv = TimeSeriesSplit(n_splits=n_splits) scores = cross_val_score(model, X, y, cv=tscv) return np.mean(scores), np.std(scores) ``` 3. **Example Usage**: ```python if __name__ == "__main__": from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier # Load sample data data = load_iris() X, y = data.data, data.target # Initialize model model = RandomForestClassifier() # Perform K-Fold Cross-Validation mean_score, std_score = k_fold_cross_validation(model, X, y) print(f"K-Fold CV - Mean Score: {mean_score}, Std Dev: {std_score}") # Perform Stratified K-Fold Cross-Validation mean_score, std_score = stratified_k_fold_cross_validation(model, X, y) print(f"Stratified K-Fold CV - Mean Score: {mean_score}, Std Dev: {std_score}") # Perform Time Series Cross-Validation mean_score, std_score = time_series_cross_validation(model, X, y) print(f"Time Series CV - Mean Score: {mean_score}, Std Dev: {std_score}") ``` ### Summary - **Import necessary libraries**: `sklearn.model_selection` and `numpy`. - **Define cross-validation functions**: Implement K-Fold, Stratified K-Fold, and TimeSeriesSplit cross-validation techniques. - **Parameterize functions**: Allow functions to accept model, data, and number of splits. - **Return results**: Ensure functions return mean and standard deviation of the scores. This implementation will provide a robust framework for validating machine learning models using different cross-validation techniques.

Changes on backend-container/docs/model_documentation.md

To address the task for the `backend-container/docs/model_documentation.md` file, you need to create comprehensive documentation that covers the following aspects: 1. **Model Architecture and Design**: - Describe the types of models used (e.g., linear regression, neural networks). - Explain the architecture of each model, including layers, activation functions, and any other relevant details. - Discuss the rationale behind choosing specific models for different analytics needs. 2. **Data Preprocessing**: - Detail the steps taken to preprocess the historical NFL data. - Include information on data cleaning, normalization, and any transformations applied. - Explain how missing values were handled and any data augmentation techniques used. 3. **Feature Engineering**: - Describe the feature engineering process, including the selection and creation of features. - Explain how feature importance was determined and any dimensionality reduction techniques used. - Provide examples of key features that were engineered and their impact on model performance. 4. **Model Training and Evaluation**: - Outline the training process for each model, including the training data, validation data, and any hyperparameter tuning performed. - Discuss the evaluation metrics used to assess model performance. - Include results from cross-validation and any other validation techniques used. 5. **Model Iteration and Improvement**: - Explain the iterative process of refining the models to improve accuracy and performance. - Discuss any challenges faced during model development and how they were addressed. - Provide insights into future improvements and potential areas for further research. 6. **Configuration and Deployment**: - Detail how model credentials and configurations are stored using GitHub Secrets. - Explain the process of deploying the models within Azure AI Studio or Microsoft Fabric. - Include any scripts or commands used for deployment. Here is a template to get you started: ```markdown # Model Documentation ## 1. Model Architecture and Design ### 1.1 Types of Models - Linear Regression - Neural Networks (TensorFlow, PyTorch) ### 1.2 Model Architecture - **Linear Regression**: Simple linear model with input features and a single output. - **Neural Networks**: - Input Layer: [Describe input features] - Hidden Layers: [Number of layers, types of layers, activation functions] - Output Layer: [Describe output] ### 1.3 Rationale for Model Selection - [Explain why specific models were chosen for different analytics needs] ## 2. Data Preprocessing ### 2.1 Data Cleaning - [Describe steps taken to clean the data] ### 2.2 Data Normalization - [Explain normalization techniques used] ### 2.3 Handling Missing Values - [Describe methods used to handle missing data] ### 2.4 Data Augmentation - [Detail any data augmentation techniques applied] ## 3. Feature Engineering ### 3.1 Feature Selection - [Describe how features were selected] ### 3.2 Feature Creation - [Explain the process of creating new features] ### 3.3 Feature Importance - [Discuss how feature importance was determined] ### 3.4 Dimensionality Reduction - [Describe any dimensionality reduction techniques used] ## 4. Model Training and Evaluation ### 4.1 Training Process - [Outline the training process] ### 4.2 Evaluation Metrics - [Discuss evaluation metrics used] ### 4.3 Cross-Validation Results - [Include cross-validation results] ## 5. Model Iteration and Improvement ### 5.1 Iterative Process - [Explain the iterative process of refining models] ### 5.2 Challenges and Solutions - [Discuss challenges faced and solutions implemented] ### 5.3 Future Improvements - [Provide insights into future improvements] ## 6. Configuration and Deployment ### 6.1 Storing Credentials - [Detail how credentials are stored using GitHub Secrets] ### 6.2 Deployment Process - [Explain the deployment process within Azure AI Studio or Microsoft Fabric] ### 6.3 Deployment Scripts - [Include any scripts or commands used for deployment] ``` This template should help you create a detailed and organized documentation file that covers all necessary aspects of the machine learning models developed for NFL analytics.

Changes on backend-container/src/models/ml_models.py

To address the task for the `backend-container/src/models/ml_models.py` file, we need to implement functions that cover the following aspects: 1. **Define Model Requirements and Objectives**: This involves setting up the goals for the machine learning models, such as predicting player performance or game outcomes. 2. **Develop and Train Models**: This includes creating and training machine learning models using Scikit-learn for simpler algorithms and TensorFlow/PyTorch for more complex neural networks. 3. **Perform Feature Engineering and Selection**: This involves preprocessing the data to enhance model performance. Here's a concrete proposal for implementing these functions: ### Step-by-Step Implementation 1. **Define Model Requirements and Objectives**: - Create a function to set up the objectives and requirements for the models. 2. **Develop and Train Models**: - Implement functions to develop and train models using Scikit-learn, TensorFlow, and PyTorch. 3. **Perform Feature Engineering and Selection**: - Implement functions to preprocess the data and select the most relevant features. ### Proposed Code ```python # backend-container/src/models/ml_models.py import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score import tensorflow as tf import torch import torch.nn as nn import torch.optim as optim def define_model_requirements(): """ Define the requirements and objectives for the machine learning models. """ requirements = { 'player_performance': { 'description': 'Predict player performance based on historical data.', 'metrics': ['yards', 'touchdowns', 'interceptions'] }, 'game_outcome': { 'description': 'Predict game outcomes based on team statistics.', 'metrics': ['win', 'loss'] } } return requirements def develop_and_train_sklearn_model(X, y): """ Develop and train a machine learning model using Scikit-learn. """ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train) y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(f'Scikit-learn Model Accuracy: {accuracy}') return model def develop_and_train_tensorflow_model(X, y): """ Develop and train a machine learning model using TensorFlow. """ model = tf.keras.Sequential([ tf.keras.layers.Dense(128, activation='relu', input_shape=(X.shape[1],)), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.fit(X, y, epochs=10, batch_size=32, validation_split=0.2) return model def develop_and_train_pytorch_model(X, y): """ Develop and train a machine learning model using PyTorch. """ class SimpleNN(nn.Module): def __init__(self): super(SimpleNN, self).__init__() self.fc1 = nn.Linear(X.shape[1], 128) self.fc2 = nn.Linear(128, 64) self.fc3 = nn.Linear(64, 1) def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) x = torch.sigmoid(self.fc3(x)) return x model = SimpleNN() criterion = nn.BCELoss() optimizer = optim.Adam(model.parameters(), lr=0.001) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) X_train, y_train = torch.tensor(X_train, dtype=torch.float32), torch.tensor(y_train, dtype=torch.float32) X_test, y_test = torch.tensor(X_test, dtype=torch.float32), torch.tensor(y_test, dtype=torch.float32) for epoch in range(10): model.train() optimizer.zero_grad() outputs = model(X_train) loss = criterion(outputs.squeeze(), y_train) loss.backward() optimizer.step() print(f'Epoch {epoch+1}, Loss: {loss.item()}') model.eval() with torch.no_grad(): outputs = model(X_test).squeeze() predicted = (outputs > 0.5).float() accuracy = (predicted == y_test).float().mean() print(f'PyTorch Model Accuracy: {accuracy.item()}') return model def perform_feature_engineering(data): """ Perform feature engineering and selection to preprocess the data. """ # Example feature engineering steps data['feature1'] = data['raw_feature1'] / data['raw_feature2'] data['feature2'] = data['raw_feature3'] * data['raw_feature4'] selected_features = ['feature1', 'feature2', 'raw_feature5'] return data[selected_features] # Example usage if __name__ == "__main__": # Load your data here data = pd.read_csv('nfl_data.csv') X = perform_feature_engineering(data) y = data['target'] # Define model requirements requirements = define_model_requirements() print(requirements) # Develop and train models sklearn_model = develop_and_train_sklearn_model(X, y) tensorflow_model = develop_and_train_tensorflow_model(X, y) pytorch_model = develop_and_train_pytorch_model(X, y) ``` ### Explanation 1. **define_model_requirements**: This function sets up the objectives and key metrics for the models. 2. **develop_and_train_sklearn_model**: This function develops and trains a RandomForest model using Scikit-learn. 3. **develop_and_train_tensorflow_model**: This function develops and trains a neural network using TensorFlow. 4. **develop_and_train_pytorch_model**: This function develops and trains a neural network using PyTorch. 5. **perform_feature_engineering**: This function performs basic feature engineering and selection. This implementation provides a solid foundation for developing and training machine learning models for NFL analytics. You can further customize and expand these functions based on specific requirements and data characteristics.

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