Programming Homework #2

• Auto ML for clustering
• This is team project
  • Made by 김도균, 김범석, 김서영

findBestClusterOptions

• Goal: This function will try combinations of the various models automatically.
  • this fucntion will find best hyperpatameter k value for k-means, EM, DBSCAN, MeanShift and CLARANS which using the best silhouette score.
  • This function let us know what scaler, model, and hyperparameter has the best silhouette score.
  • This function was documented by pydoc.

Parameters

• X: pandas.DataFrame
  • training dataset.
• scalers: array
  • Scaler functions to scale data. This can be modified by user.
  • None, StandardScaler() as default
  • This parameter is compatible with StandardScaler, RobustScaler, MinMaxScaler, MaxAbsScaler.
• models: array
  • Model functions to clustering data. This can be modified by user.
  • KMeans, GaussianMixture, DBSCAN(eps=0.5, min_samples=5) as default with hyperparameters.
  • This parameter is compatible with KMeans, GaussianMixture, DBSCAN, CLARANS, MeanShift.
• cluster_k: array
  • Number of cluster. Default value is [3].
  • This can be modified by user.

Returns

• best_params_: dictionary
  • best_scaler_: Scaler what has best silhouette score.
  • best_model_: Model what has best silhouette score.
  • best_k_: Best number of clusters
• best_score_: double
  • Represent the silhouette score of the best_params.

Example

result = findBestOptions(
    df, 
    models=[
    CLARANS(data=df.to_numpy(), number_clusters=1, numlocal=2, maxneighbor=3),
    GaussianMixture(),
    KMeans(),
    DBSCAN(eps=0.5, min_samples=5),
    MeanShift(bandwidth=bandwidth)
    ],
    scalers=[None,], #StandardScaler(), RobustScaler(), MinMaxScaler(), MaxAbsScaler() 
    cluster_k = range(2,11)
)

# Extract results
labels = result['labels_']
best_score = result['best_score_']
result = result['best_params_']
best_scaler = result['best_scaler_']
best_model = result['best_model_']
best_k = result['best_k_']

# Print the result of best option
print("\nBest Scaler: ", end="")
print(best_scaler)
print("Best Model: ", end="")
print(best_model)
print("Score: ", end="")
print(best_score)
print("labels: ", end="")
print(labels)
print("k: ", end="")
print(best_k)