Over 153.5 Points basketball predictions today (2025-08-23)

"Don’t forget about home-court advantage; Team A has been particularly strong at home this season." - HoopsAnalyst88

[0]: #!/usr/bin/env python3 [1]: # -*- coding: utf-8 -*- [2]: """ [3]: Created on Tue Nov 19th [4]: @author: mohammed-ismail [5]: """ [6]: import sys [7]: import os [8]: import numpy as np [9]: import pandas as pd [10]: import matplotlib.pyplot as plt [11]: from sklearn.model_selection import train_test_split [12]: from sklearn.metrics import confusion_matrix [13]: from keras.utils import np_utils [14]: from keras.models import Sequential [15]: from keras.layers import Dense [16]: from keras.optimizers import SGD [17]: np.random.seed(0) [18]: # Loading dataset [19]: dataset = pd.read_csv('iris.data', header=None) [20]: # Converting dataframe into array [21]: dataset = dataset.values [22]: # Printing shape of dataset [23]: print(dataset.shape) [24]: # Assigning features & labels [25]: X = dataset[:,0:4].astype(float) [26]: y = dataset[:,4] [27]: # Splitting into training & test set [28]: X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25) [29]: # Printing shapes [30]: print(X_train.shape) [31]: print(X_test.shape) [32]: print(y_train.shape) [33]: print(y_test.shape) [34]: # Encoding categorical integer features using one-hot encoding [35]: encoder = LabelEncoder() [36]: encoder.fit(y_train) [37]: y_train_enc = encoder.transform(y_train) [38]: y_test_enc = encoder.transform(y_test) ***** Tag Data ***** ID: N/A description: The code block involving one-hot encoding is not fully implemented, but it shows an advanced technique that would be useful when working with categorical data. start line: 34 end line: 37 dependencies: - type: Other name: LabelEncoder start line: -1 end line: -1 context description: This snippet attempts to use one-hot encoding but lacks completeness. algorithmic depth: 4 algorithmic depth external: N obscurity: 3 advanced coding concepts: 3 interesting for students: 4 self contained: Y ************ ## Challenging aspects ### Challenging aspects in above code 1. **Label Encoding vs One-Hot Encoding**: The snippet uses `LabelEncoder`, which encodes labels into integers instead of using one-hot encoding directly as suggested by its comment line. 2. **Data Pipeline Integration**: Ensuring seamless integration within a larger data processing pipeline requires careful handling of data formats and transformations. 3. **Handling Unseen Labels**: During transformation (`transform` method), unseen labels might cause errors if not handled properly. 4. **Scalability**: Efficiently encoding large datasets while maintaining performance is crucial. 5. **Maintaining Data Integrity**: Ensuring that transformations preserve original data relationships (e.g., label distribution). ### Extension 1. **One-Hot Encoding Implementation**: Extend functionality to perform true one-hot encoding. 2. **Handling New Labels**: Implement mechanisms to handle unseen labels during transformation. 3. **Batch Processing**: Handle large datasets by processing them in batches. 4. **Integration with Scikit-Learn Pipelines**: Ensure compatibility with Scikit-Learn pipelines for streamlined workflow integration. ## Exercise ### Full Exercise Description You are tasked with extending a given code snippet [SNIPPET] that currently uses `LabelEncoder` but needs actual one-hot encoding functionality. ### Requirements: 1. **Implement One-Hot Encoding**: - Extend [SNIPPET] so that it performs true one-hot encoding. - Use `OneHotEncoder` from Scikit-Learn. 2. **Handle Unseen Labels**: - Ensure that during transformation (`transform`), unseen labels do not cause errors. - Introduce a parameter `handle_unknown` that decides whether unseen labels should raise an error (`'error'`) or be ignored (`'ignore'`). 3. **Batch Processing**: - Implement functionality to process large datasets in batches. - Provide an interface where users can specify batch size. 4. **Integration**: - Ensure compatibility with Scikit-Learn pipelines by implementing `fit`, `transform`, `fit_transform` methods appropriately. ### Provided Snippet: python # [SNIPPET] # Converting categorical integer features using one-hot encoding encoder = LabelEncoder() encoder.fit(y_train) y_train_enc = encoder.transform(y_train) ### Solution: python import numpy as np from sklearn.preprocessing import OneHotEncoder class AdvancedOneHotEncoder: def __init__(self, handle_unknown='error', batch_size=None): self.encoder = OneHotEncoder(sparse=False) self.handle_unknown = handle_unknown self.batch_size = batch_size def fit(self, y): self.encoder.fit(np.array(y).reshape(-1,1)) def transform(self, y): try: return self.encoder.transform(np.array(y).reshape(-1,1)) except ValueError as e: if self.handle_unknown == 'error': raise e if self.handle_unknown == 'ignore': known_labels = self.encoder.categories_[0] y_transformed = [] for label in y: if label in known_labels: y_transformed.append(label) else: y_transformed.append(None) # or some default handling return self.encoder.transform(np.array([x for x in y_transformed if x is not None]).reshape(-1,1)) def fit_transform(self, y): self.fit(y) return self.transform(y) # Example usage: y_train = ['cat', 'dog', 'fish', 'cat'] y_test = ['dog', 'bird'] encoder = AdvancedOneHotEncoder(handle_unknown='ignore') encoder.fit(y_train) y_train_enc = encoder.transform(y_train) y_test_enc = encoder.transform(y_test) print("Encoded Training Data:n", y_train_enc) print("Encoded Test Data:n", y_test_enc) ## Follow-up exercise ### Adding Layers of Complexity: 1. **Custom Handling for Unseen Labels**: - Modify your implementation such that unseen labels during transformation are mapped to a specific custom vector (e.g., all zeros). 2. **Multi-Class Handling**: - Extend functionality so it can handle multi-class multi-label scenarios where each sample might belong to multiple classes simultaneously. 3. **Integration Test**: - Write integration tests ensuring compatibility within Scikit-Learn pipelines including grid search functionalities (`GridSearchCV`). ## Solution: python import numpy as np from sklearn.preprocessing import OneHotEncoder class AdvancedOneHotEncoder: def __init__(self, handle_unknown='error', batch_size=None): self.encoder = OneHotEncoder(sparse=False) self.handle_unknown = handle_unknown self.batch_size = batch_size def fit(self, y): self.encoder.fit(np.array(y).reshape(-1,1)) def transform(self, y): try: return self.encoder.transform(np.array(y).reshape(-1,1)) except ValueError as e: if self.handle_unknown == 'error': raise e if self.handle_unknown == 'ignore': known_labels = self.encoder.categories_[0] y_transformed = [] for label in y: if label in known_labels: y_transformed.append(label) else: y_transformed.append(None) # or some default handling return self.encoder.transform(np.array([x for x in y_transformed if x is not None]).reshape(-1,1)) elif self.handle_unknown == 'custom_vector': known_labels = self.encoder.categories_[0] custom_vector_length = len(known_labels) custom_vector = np.zeros((custom_vector_length,)) transformed_data = [] for label in y: if label in known_labels: transformed_data.append(label)