Metadata-Version: 2.2
Name: utilsds
Version: 1.1.0
Summary: Solution for DS Team
Author: DS Team
Author-email: ds@sts.pl
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.11
Description-Content-Type: text/markdown
Requires-Dist: pandas>=2.2.2
Requires-Dist: numpy>=1.26.4
Requires-Dist: scikit-learn>=1.4.2
Requires-Dist: seaborn>=0.13.2
Requires-Dist: matplotlib>=3.9.0
Requires-Dist: google-cloud-bigquery>=3.22.0
Requires-Dist: google-cloud-bigquery-storage>=2.0.0
Requires-Dist: google-cloud-storage>=2.16.0
Requires-Dist: google-cloud-aiplatform>=1.51.0
Requires-Dist: scipy>=1.13.0
Requires-Dist: hyperopt>=0.2.7
Requires-Dist: tqdm>=4.66.4
Requires-Dist: xgboost>=1.7.6
Requires-Dist: lightgbm>=4.0.0
Requires-Dist: yellowbrick>=1.5
Requires-Dist: cloudpickle>=2.3.0
Requires-Dist: db-dtypes>=1.4.0
Requires-Dist: pygments>=2.19.1
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# utilsds

Utilsds is a library that includes classes and functions used in data science projects such as:

- **ds_statistics**: 
  - `test_kruskal_wallis`: Perform the Kruskal-Wallis statistical test.

- **transform_data**: 
  - `DataTransformer`: Transform data using various methods.

- **data_processing**:
  - `encode_one_hot`: Encode categorical features using one-hot encoding.
  - `convert_numerical_to_categorized`: Convert numerical features to categorized intervals.
  - `scale_train_test`: Scale training and testing datasets.
  - `resample_X_y`: Resample training data and target columns.

- **data_split**: 
  - `train_test_validation_split`: Split data into training, testing, and validation sets.

- **visualization**: 
  - `MetricsPlot`: Compare metrics for different parameter values.
  - `Radar`: Create radar plots for visualizing data.
  - `cluster_characteristics`: Analyze cluster characteristics.
  - `comparison_density`: Compare density distributions.
  - `feature_distribution_box`: Visualize feature distributions per cluster.
  - `elbow_visualisation`: Visualize the elbow method for clustering.
  - `describe_clusters_metrics`: Describe metrics for clusters.
  - `category_null_variables`: Visualize null variables in categorical data.
  - `normal_distr_plots`: Visualize normal distribution plots.
  - `distplot_limitations`: Visualize limitations of distplot.
  - `boxplot_limitations`: Visualize limitations of boxplot.
  - `violinplot_limitations`: Visualize limitations of violinplot.
  - `countplot_limitations`: Visualize limitations of countplot.
  - `categorical_variable_perc`: Visualize percentage of categorical variables.
  - `spearman_correlation`: Visualize spearman correlation.
  - `CalculateCrammersV`: Calculate Crammer's V.


- **data_ops**:
    - BigQuery operations:
      - `load_bq_data`: Load data from tables, views, and SQL files
      - `save_bq_view`, `save_bq_table`: Save views and tables
      - `load_bq_procedure`: Execute stored procedures
      - `load_bq_details`: Get table/view details and schema
      - `load_bq_describe_data`: Get data description using ML.DESCRIBE_DATA
      - `delete_bq_data`: Delete data with safety confirmations
      - `dry_run`: Perform dry runs to estimate query costs
    - Cloud Storage operations:
      - `save_gcs_bucket`: Create buckets
      - `save_gcs_file`, `load_gcs_file`: Save and load files (.pkl, .json, .csv, .html, .sql)
    - Local file operations:
      - `save_local_file`, `load_local_file`: Save and load files (.pkl, .json, .csv, .html, .sql)

- **confusion_matrix**:
  - `ConfusionMatrix`: Generate and plot confusion matrices.

- **modeling**:
  - `Modeling`: Manage modeling, metrics, and logging with Vertex AI.

- **hyperopt**:
  - `Hyperopt`: Optimize hyperparameters using Hyperopt.

- **classifier**:
  - `Classifier`: Fit, train, and manage classification models.

- **experiments**:
  - `VertexExperiments`: Manage experiments with Vertex AI.
