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question:**Objective:** The goal of this question is to demonstrate your understanding of scikit-learn transformers and how to use them for data preprocessing, feature extraction, and combining transformations. **Question:** You are provided with a dataset containing numerical and categorical features. Your task is to preprocess this data by performing the following transformations: 1. Impute missing numerical values with the mean of the respective columns. 2. Standardize all numerical features (zero mean, unit variance). 3. Encode categorical features using one-hot encoding. 4. Combine the transformations into a single pipeline that can handle the given dataset and output preprocessed data ready for a machine learning model. **Dataset:** Assume the dataset is loaded into a pandas DataFrame `df` with the following columns: - `feature1` (numerical) - `feature2` (numerical) - `feature3` (categorical) - `feature4` (categorical) **Requirements:** 1. Implement a function `preprocess_data(df: pd.DataFrame) -> np.ndarray` that takes a pandas DataFrame as input and returns the preprocessed data as a numpy ndarray. 2. Use `SimpleImputer` for imputing missing values and `StandardScaler` for standardization. 3. Use `OneHotEncoder` for encoding categorical features. 4. Combine these transformations in a single `ColumnTransformer` and integrate them into a `Pipeline`. 5. Ensure that the pipeline can be fit and transformed on the provided dataset. **Constraints:** - Ensure the code is efficient and follows scikit-learn best practices. - You may assume that the dataset is not excessively large and fits into memory. - Handle any potential issues that might arise with missing values or categorical encoding. **Function Signature:** ```python import pandas as pd import numpy as np from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline def preprocess_data(df: pd.DataFrame) -> np.ndarray: pass ``` **Example:** ```python import pandas as pd # Example dataset data = { "feature1": [1.0, 2.0, np.nan, 4.0], "feature2": [np.nan, 2.5, 3.5, 4.5], "feature3": ["A", "B", "A", "B"], "feature4": ["X", "Y", "X", np.nan] } df = pd.DataFrame(data) # Expected output: Preprocessed data as np.ndarray processed_data = preprocess_data(df) print(processed_data) ``` **Note:** The actual values in the `processed_data` array will depend on the specific preprocessing steps applied, but it should be a fully numeric array suitable for feeding into a machine learning model.
answer:import pandas as pd import numpy as np from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline def preprocess_data(df: pd.DataFrame) -> np.ndarray: # Split columns into numerical and categorical numerical_features = ['feature1', 'feature2'] categorical_features = ['feature3', 'feature4'] # Define transformations for numerical data numerical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler()) ]) # Define transformations for categorical data categorical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='constant', fill_value='missing')), ('onehot', OneHotEncoder(handle_unknown='ignore')) ]) # Combine both transformers into a single ColumnTransformer preprocessor = ColumnTransformer( transformers=[ ('num', numerical_transformer, numerical_features), ('cat', categorical_transformer, categorical_features) ] ) # Fit the preprocessor and transform the data processed_data = preprocessor.fit_transform(df) return processed_data
question:# Coding Assessment Question Implement a function `extract_non_empty_lines(filename: str) -> list` that reads all lines from the given file named `filename` and returns a list containing only the non-empty lines. This function should use the functionalities of the `linecache` module for line retrieval. Function Signature ```python def extract_non_empty_lines(filename: str) -> list: pass ``` Input - `filename` (str): The path to the file from which lines should be read. Output - A list of strings, each representing a non-empty line from the file. Constraints - You may assume that the file contains at most 10,000 lines. - Lines are considered non-empty if they contain any characters other than whitespace. Example Suppose `example.txt` contains the following lines: ``` This is line 1 This is line 3 # This is line 5 (whitespace before comment) ``` Calling `extract_non_empty_lines('example.txt')` should return: ``` ['This is line 1', 'This is line 3', '# This is line 5 (whitespace before comment)'] ``` Notes - Use the `linecache.getline(filename, lineno)` function to read each line from the file. - Avoid reading the entire file into memory at once. Instead, handle one line at a time. - Use `linecache.clearcache()` to clear the cache if necessary. Tips - You can use `str.strip()` to check if a line is non-empty. - Consider using `linecache.checkcache()` to ensure the file has not changed if reading it multiple times.
answer:import linecache def extract_non_empty_lines(filename: str) -> list: Reads all lines from the given file and returns a list of non-empty lines. Args: filename (str): The path to the file from which lines should be read. Returns: list: A list of non-empty lines from the file. non_empty_lines = [] lineno = 1 while True: line = linecache.getline(filename, lineno) if not line: break if line.strip(): non_empty_lines.append(line.strip()) lineno += 1 linecache.clearcache() return non_empty_lines
question:Objective: Implement a function to create, record, and manipulate events using PyTorch's distributed elastic events API. This assessment will test your understanding of the event handling mechanisms within PyTorch. Description: You are required to implement the function `manage_pytorch_events` which demonstrates the following tasks: 1. Create an event with a given `event_name` and `event_source`. 2. Add metadata to the event. 3. Record the event. 4. Retrieve and return the logging handler. Function Signature: ```python import torch.distributed.elastic.events.api as events_api def manage_pytorch_events(event_name: str, event_source: str, metadata: dict) -> events_api.Event: # Your implementation here ``` Input: - `event_name` (str): The name of the event to be created. - `event_source` (str): The source of the event. - `metadata` (dict): A dictionary containing key-value pairs to be added as metadata to the event. Output: - Returns an `Event` object with the attached metadata and recorded using the provided PyTorch events API. Constraints: - The `event_name` and `event_source` are non-empty strings. - The `metadata` dictionary contains key-value pairs where both keys and values are strings. Example: ```python event_name = "TestEvent" event_source = "TestSource" metadata = { "key1": "value1", "key2": "value2" } event = manage_pytorch_events(event_name, event_source, metadata) print(event.name) # Expected Output: TestEvent print(event.source) # Expected Output: TestSource print(event.metadata["key1"]) # Expected Output: value1 print(event.metadata["key2"]) # Expected Output: value2 ``` Notes: - Ensure that the function correctly handles the creation, metadata addition, and recording of the event. - You may refer to the PyTorch distributed elastic events API documentation for more details on the methods and classes used in this task.
answer:import torch.distributed.elastic.events.api as events_api def manage_pytorch_events(event_name: str, event_source: str, metadata: dict) -> events_api.Event: Manage PyTorch events by creating, adding metadata, and recording the event. Args: event_name (str): The name of the event to be created. event_source (str): The source of the event. metadata (dict): A dictionary containing key-value pairs to be added as metadata to the event. Returns: events_api.Event: The created event with attached metadata. # Creating the event event = events_api.Event(name=event_name, source=event_source) # Adding metadata to the event for key, value in metadata.items(): event.metadata[key] = value # Recording the event event.record() return event
question:# Question: Implement Custom Pickling for a Complex Data Structure You are given a complex data structure `CustomList`, which essentially behaves like a Python list but adds some custom behavior. Your task is to make this custom data structure pickleable using Python's `copyreg` module. Objectives 1. Implement the `CustomList` class. 2. Write a custom pickling function for instances of `CustomList`. 3. Register this custom pickling function using `copyreg`. # Class Requirements Implement a class `CustomList` that: * Behaves like a list (supports indexing, appending, etc.). * Has an additional attribute `metadata` which stores a dictionary of metadata information. Methods to Implement - `__init__(self, data, metadata)`: Initializes the `CustomList` with `data` (a list of elements) and `metadata` (a dictionary). - Other necessary methods to ensure the class behaves like a list (such as `append`, etc.) # Custom Pickling Function Implement a function `pickle_custom_list(obj)` that: * Takes an instance of `CustomList`. * Returns the constructor of `CustomList` and a tuple with the data and metadata, i.e., it should return `CustomList, (obj.data, obj.metadata)`. # Register Pickling Function Use `copyreg.pickle` to register the `CustomList` type with your `pickle_custom_list` function. Constraints * You can assume metadata keys and values are always strings. * Implement the solution using Python standard libraries (`copyreg`, `pickle`). Example ``` >>> import copyreg, pickle >>> class CustomList: ... def __init__(self, data, metadata): ... self.data = data ... self.metadata = metadata ... def append(self, item): ... self.data.append(item) >>> def pickle_custom_list(obj): ... return CustomList, (obj.data, obj.metadata) >>> copyreg.pickle(CustomList, pickle_custom_list) ... >>> cl = CustomList([1, 2, 3], {'author': 'Jane Doe'}) >>> p = pickle.dumps(cl) >>> print(p) >>> cl_loaded = pickle.loads(p) >>> print(cl_loaded.data) [1, 2, 3] >>> print(cl_loaded.metadata) {'author': 'Jane Doe'} ``` # Evaluation Criteria * Correct implementation of the `CustomList` class. * Correct implementation of the pickling function. * Accurate registration of the pickling function. * Code should be syntactically correct and follow standard Python best practices.
answer:import copyreg import pickle class CustomList: def __init__(self, data, metadata): self.data = data self.metadata = metadata def append(self, item): self.data.append(item) def __getitem__(self, index): return self.data[index] def __setitem__(self, index, value): self.data[index] = value def __len__(self): return len(self.data) def pickle_custom_list(obj): return CustomList, (obj.data, obj.metadata) copyreg.pickle(CustomList, pickle_custom_list)