Streamline your data science journey from raw data to insightful analytics.
MadCat is a versatile tool designed to simplify and automate data science tasks, encapsulating everything from video and audio data processing to advanced analytics.
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Chester Module: Simplifying complex data science tasks.
- Streamlined data pre-processing for efficient model training.
- Model training and evaluation made easy with intuitive controls.
- Comprehensive post-model analysis to identify model strengths and weaknesses.
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Diamond Module: Comprehensive image processing solutions.
- Image description: Extract insightful metadata from images.
- Object detection: Accurately classify objects within images.
- Face detection: Recognize and pinpoint faces in images.
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UDI Module: Turning audio data into actionable insights.
- Speech-to-text: Transcribe audio sequences for text analysis.
- Extract crucial features like MFCC for audio classification.
- Enhanced integration capabilities with the Chester analytics suite.
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Vis Module: A holistic approach to video data analytics.
- Grayscale conversion for enhanced video processing.
- Object tracking across frames for motion analysis.
- Adjust and inspect videos with the zoom functionality.
pip install MadCat
Chester, as a cornerstone of MadCat, simplifies the process of data science, taking you from raw data to insightful model analytics with ease. Let's delve deeper into its main function:
The run() function is a comprehensive method designed to process and analyze your data seamlessly.
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data_spec (Data): The main data input.
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feature_types (dict, optional): Dictionary specifying the types of features (e.g., categorical, numerical, etc.)
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text_handler (TextHandler, optional): Manages text data transformations.
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is_text_handler (bool, default=True): Flag to determine if text handler should be used.
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text_summary (TextSummary, optional): Provides summary statistics and insights for text data.
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time_series_handler (TimeSeriesHandler, optional): Manages time series data transformations.
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is_time_series_handler (bool, default=True): Flag to determine if time series handler should be used.
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feature_stats (FeatureStats, optional): Provides feature statistics.
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is_feature_stats (bool, default=True): Flag to determine if feature statistics should be used.
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text_feature_extraction (TextFeatureExtraction, optional): Deals with text data processing and feature extraction.
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is_feature_extract (bool, default=True): Flag to determine if text feature extraction should be used.
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is_pre_model (bool, default=True): Flag to determine if pre-modeling processes should be initiated.
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is_model_training (bool, default=True): Flag to determine if model training should be initiated.
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model_run (ModelRun, optional): Manages and controls the model's lifecycle from training to testing.
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is_post_model (bool, default=True): Flag to determine if post-modeling processes should be initiated.
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is_model_weaknesses (bool, default=True): Flag to determine if model weaknesses should be analyzed.
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plot (bool, default=True): Flag to determine if plots should be generated.
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max_stats_col_width (int, default=30): Maximum column width for statistics display.
Using Chester's run() function, you can integrate, preprocess, train, and analyze your models effortlessly.
MadCat's primary goal is to uncomplicate complex data science challenges. To see it in action:
import pandas as pd
from chester.run.full_run import run
# Load the Iris dataset
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = pd.read_csv(url, names=names)
# Rename target column and shuffle dataset
dataset.rename(columns={'class': 'target'}, inplace=True)
df = dataset.sample(frac=1).reset_index(drop=True)
# Run MadCat on the dataset
run_metadata_collector = run(
data=df,
target_column='target'
)Released under the MIT License.