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Tecnología e Innovación: Cómo Cambian las Reglas del Juego

[0]: import numpy as np [1]: import pandas as pd [2]: import os [3]: import pickle [4]: import time [5]: from sklearn.metrics import roc_auc_score [6]: from sklearn.metrics import log_loss [7]: from sklearn.metrics import mean_squared_error [8]: from sklearn.preprocessing import OneHotEncoder [9]: from deepctr_torch.inputs import SparseFeat, DenseFeat [10]: from deepctr_torch.models import DCN [11]: from IPython.core.interactiveshell import InteractiveShell [12]: InteractiveShell.ast_node_interactivity = "all" [13]: os.environ["CUDA_VISIBLE_DEVICES"] = "0" [14]: data_path = "./data/" [15]: train_data = pd.read_csv(data_path + 'train.csv', usecols=['ip', 'app', 'device', 'os', 'channel', 'click']) [16]: test_data = pd.read_csv(data_path + 'test.csv', usecols=['ip', 'app', 'device', 'os', 'channel']) [17]: feature_columns = get_feature_columns(train_data) [18]: train_model_input = {name:train_data[name].values for name in train_data.columns if name != "click"} [19]: train_model_input['click'] = train_data['click'].values [20]: test_model_input = {name:test_data[name].values for name in test_data.columns} [21]: model = DCN(feature_columns=feature_columns, [22]: dnn_hidden_units=[256,128,64], [23]: dnn_dropout=0, [24]: cin_layer_units=[32], [25]: cin_activation='relu', [26]: task='binary_classification', [27]: device='cpu' [28]: ) [29]: model.compile("adam", "binary_crossentropy", metrics=['binary_crossentropy']) [30]: model.fit(train_model_input, [31]: batch_size=256, [32]: epochs=1, [33]: verbose=True) ***** Tag Data ***** ID: 3 description: This snippet defines and initializes a Deep & Cross Network (DCN) model. start line: 21 end line: 28 dependencies: - type: Function/Method name: DCN start line: 10 end line: 10 context description: The snippet creates an instance of the DCN model with specified hyperparameters and settings. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 4 interesting for students: 5 self contained: N ************ ## Challenging aspects ### Challenging aspects in above code 1. **Feature Columns Handling**: The `feature_columns` parameter is expected to be correctly structured and comprehensive enough to represent all necessary features for the DCN model. Students must ensure that feature columns are meticulously prepared to avoid any downstream issues in model training. 2. **Hyperparameter Tuning**: The hyperparameters such as `dnn_hidden_units`, `dnn_dropout`, `cin_layer_units`, and `cin_activation` need careful tuning to optimize the model performance for binary classification tasks. 3. **Device Management**: Specifying `device='cpu'` requires understanding the implications on computational efficiency and potential bottlenecks compared to GPU usage. 4. **Task Specification**: Setting `task='binary_classification'` implies that students must ensure their dataset and labels are suitable for binary classification tasks. 5. **Integration with Other Components**: Integrating this model into a larger pipeline that may include data preprocessing steps and evaluation metrics adds complexity. ### Extension 1. **Dynamic Feature Engineering**: Introduce dynamic feature engineering where new features can be generated during runtime based on existing data characteristics. 2. **Adaptive Hyperparameter Tuning**: Implement an adaptive mechanism that tunes hyperparameters during training based on validation performance. 3. **Multi-Task Learning**: Extend the model to handle multi-task learning scenarios where it can simultaneously perform multiple tasks like binary classification and regression. 4. **Custom Activation Functions**: Allow users to define custom activation functions beyond standard ones like `relu`. 5. **Advanced Device Management**: Introduce functionality to dynamically switch between CPU and GPU based on resource availability. ## Exercise ### Problem Statement You are tasked with extending the functionality of the provided DCN model snippet [SNIPPET] by implementing dynamic feature engineering and adaptive hyperparameter tuning mechanisms. #### Requirements: 1. **Dynamic Feature Engineering**: - Implement a function that takes raw input data and generates new features dynamically based on existing ones. - Ensure these new features are incorporated into `feature_columns`. 2. **Adaptive Hyperparameter Tuning**: - Create an adaptive mechanism that adjusts `dnn_hidden_units` and `dnn_dropout` based on validation set performance after each epoch. - Implement logic to increase or decrease the number of units in hidden layers and adjust dropout rates accordingly. 3. **Custom Activation Function**: - Allow users to specify custom activation functions via an additional parameter. - Ensure these custom functions can be integrated seamlessly into both DNN and CIN layers. ### Exercise Code: python from deepctr_torch.models import DCN # Dynamic Feature Engineering Function def dynamic_feature_engineering(data): # Implement feature engineering logic here. # For example, create interaction terms or polynomial features dynamically. new_features = ... return new_features # Adaptive Hyperparameter Tuning Function def adaptive_hyperparameter_tuning(model, validation_data): # Logic to adjust hyperparameters based on validation performance. current_performance = evaluate_model(model, validation_data) if current_performance improves: # Adjust hyperparameters positively. model.dnn_hidden_units = [units * factor for units in model.dnn_hidden_units] model.dnn_dropout *= factor_decrease else: # Adjust hyperparameters negatively. model.dnn_hidden_units = [max(units // factor_downscale, min_units) for units in model.dnn_hidden_units] model.dnn_dropout *= factor_increase # Custom Activation Function Wrapper def custom_activation_function(x): # Define your custom activation function logic here. return ... # Main Snippet Extension class ExtendedDCN(DCN): def __init__(self, feature_columns=None, dnn_hidden_units=None, dnn_dropout=0., cin_layer_units=None, cin_activation='relu', task='binary_classification', device='cpu', custom_activation=None): super().__init__(feature_columns=feature_columns, dnn_hidden_units=dnn_hidden_units, dnn_dropout=dnn_dropout, cin_layer_units=cin_layer_units, cin_activation=cin_activation, task=task, device=device) self.custom_activation = custom_activation if custom_activation else cin_activation # Usage Example: feature_columns = dynamic_feature_engineering(raw_data) model = ExtendedDCN(feature_columns=feature_columns, dnn_hidden_units=[256,128,64], dnn_dropout=0, cin_layer_units=[32], cin_activation=custom_activation_function, task='binary_classification', device='cpu') for epoch in range(num_epochs): train_model(model) # Perform adaptive hyperparameter tuning based on validation set performance. adaptive_hyperparameter_tuning(model, validation_data) ## Solution