Open zepor opened 1 month ago
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.
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
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
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)
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
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)
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]
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:
Develop Machine Learning Models for NFL Analytics
Description: Develop and train machine learning models using historical NFL data. Tasks:
AZURE_ML_WORKSPACE_CONNECTION
,AZURE_ML_MODEL_REGISTRY
. Milestone: Machine Learning models developed and trained.