so4gp.algorithms.cluster_gp.ClusterGP

class ClusterGP(*args, e_prob=0.5, max_iter=10, **kwargs)

Parameters:

• args – [required] data source path of Pandas DataFrame, [optional] minimum-support, [optional] eq

• e_prob (float) – [optional] erasure probability, the default is 0.5

• max_iter (int) – [optional] maximum iteration for score vector estimation, the default is 10

>>> import pandas
>>> from so4gp.algorithms import ClusterGP
>>> dummy_data = [[30, 3, 1, 10], [35, 2, 2, 8], [40, 4, 2, 7], [50, 1, 1, 6], [52, 7, 1, 2]]
>>> dummy_df = pandas.DataFrame(dummy_data, columns=['Age', 'Salary', 'Cars', 'Expenses'])
>>>
>>> mine_obj = ClusterGP(data_source=dummy_df, min_sup=0.5, max_iter=3, e_prob=0.5)
>>> result_json = mine_obj.discover()
>>> print(result_json)

__init__(*args, e_prob=0.5, max_iter=10, **kwargs)

CluDataGP stands for Clustering DataGP. It is a class that inherits the DataGP class to create data-gp objects for the clustering approach. This class inherits the DataGP class which is used to create data-gp objects. The classical data-gp object is meant to store all the parameters required by GP algorithms to extract gradual patterns (GP). It takes a numeric file (in CSV format) as input and converts it into an object whose attributes are used by algorithms to extract GPs.

A class for creating data-gp objects for the clustering approach. This class inherits the DataGP class which is used to create data-gp objects. This class adds the parameters required for clustering gradual items to the data-gp object.

Parameters:

• args – [required] data source path of Pandas DataFrame, [optional] minimum-support, [optional] eq

• e_prob (float) – [optional] erasure probability, the default is 0.5

• max_iter (int) – [optional] maximum iteration for score vector estimation, the default is 10

>>> import pandas
>>> from so4gp.algorithms import ClusterGP
>>> dummy_data = [[30, 3, 1, 10], [35, 2, 2, 8], [40, 4, 2, 7], [50, 1, 1, 6], [52, 7, 1, 2]]
>>> dummy_df = pandas.DataFrame(dummy_data, columns=['Age', 'Salary', 'Cars', 'Expenses'])
>>>
>>> mine_obj = ClusterGP(data_source=dummy_df, min_sup=0.5, max_iter=3, e_prob=0.5)
>>> result_json = mine_obj.discover()
>>> print(result_json)

Methods

__init__(*args[, e_prob, max_iter])	CluDataGP stands for Clustering DataGP.
add_gradual_pattern(pattern)	Adds a gradual pattern to the list of gradual patterns.
analyze_gps(data_src, min_sup, est_gps[, ...])	For each estimated GP, computes its true support using the GRAANK approach and returns the statistics (% error, and standard deviation).
clean_data(df)	Cleans a data-frame (i.e., missing values, outliers) before extraction of GPs
clear_gradual_patterns()	Clears the list of gradual patterns.
discover()	Applies spectral clustering to determine which gradual items belong to the same group based on the similarity of net-win vectors.
fit_bitmap([attr_data])	Generates bitmaps for columns with numeric objects.
fit_warpingset()	Generates transaction ids (tids) for each column/feature with numeric objects.
gen_gradual_warping_set(pairwise_mat[, as_array])	A method that decomposes the pairwise matrix of a gradual item/pattern into a warping set.
generate_output_files(alg_data[, ...])	Generates output of results (as files) for the GP mining algorithm.
read(data_src)	Reads all the contents of a file (in CSV format) or a data-frame.
remove_subsets(gi_arr[, gradual_patterns])	Remove subset GPs from the list.
test_time(date_str)	Tests if a str represents a date-time variable.

Attributes

attr_cols
attr_size
col_count
data
display_patterns
display_patterns_as_df
gradual_patterns
row_count
thd_supp
time_cols
titles
valid_bins
warping_set