so4gp.algorithms.tgrad.TGrad

class TGrad(*args, target_col, min_rep=0.5, **kwargs)

Parameters:

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

• target_col (int) – [required] Target column.

• min_rep (float) – [optional] minimum representativity value.

>>> import so4gp.algorithms import TGrad
>>> import pandas
>>>
>>> dummy_data = [["2021-03", 30, 3, 1, 10], ["2021-04", 35, 2, 2, 8], ["2021-05", 40, 4, 2, 7], ["2021-06", 50, 1, 1, 6], ["2021-07", 52, 7, 1, 2]]
>>> dummy_df = pandas.DataFrame(dummy_data, columns=['Date', 'Age', 'Salary', 'Cars', 'Expenses'])
>>>
>>> mine_obj = TGrad(dummy_df, min_sup=0.5, target_col=1, min_rep=0.5)
>>> result_json = mine_obj.discover_tgp(parallel=True)
>>> # print(result['Patterns'])
>>> print(result_json)

__init__(*args, target_col, min_rep=0.5, **kwargs)

TGrad is an algorithm used to extract temporal gradual patterns from numeric datasets. An algorithm for mining temporal gradual patterns using fuzzy membership functions. It uses a technique published in: https://ieeexplore.ieee.org/abstract/document/8858883.

Parameters:

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

• target_col (int) – [required] Target column.

• min_rep (float) – [optional] minimum representativity value.

>>> import so4gp.algorithms import TGrad
>>> import pandas
>>>
>>> dummy_data = [["2021-03", 30, 3, 1, 10], ["2021-04", 35, 2, 2, 8], ["2021-05", 40, 4, 2, 7], ["2021-06", 50, 1, 1, 6], ["2021-07", 52, 7, 1, 2]]
>>> dummy_df = pandas.DataFrame(dummy_data, columns=['Date', 'Age', 'Salary', 'Cars', 'Expenses'])
>>>
>>> mine_obj = TGrad(dummy_df, min_sup=0.5, target_col=1, min_rep=0.5)
>>> result_json = mine_obj.discover_tgp(parallel=True)
>>> # print(result['Patterns'])
>>> print(result_json)

Methods

__init__(*args, target_col[, min_rep])	TGrad is an algorithm used to extract temporal gradual patterns from numeric datasets.
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).
build_mf_w_clusters(time_data)	A method that builds the boundaries of a fuzzy Triangular membership function (MF) using Singular Value Decomposition (to estimate the number of centers) and KMeans algorithm to group time data according to the identified centers.
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([ignore_support, apriori_level, ...])	Uses apriori algorithm to find gradual pattern (GP) candidates.
discover_tgp([parallel, num_cores])	Applies fuzzy-logic, data transformation, and gradual pattern mining to mine for Fuzzy Temporal Gradual Patterns.
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.
get_fuzzy_time_lag(bin_data, time_data[, ...])	A method that uses a fuzzy membership function to select the most accurate time-delay value.
get_time_diffs(step)	A method that computes the difference between 2 timestamps separated by a specific transformation step.
get_timestamp(time_str)	A method that computes the corresponding timestamp from a DateTime string.
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.
transform_and_mine(step[, return_patterns])	A method that: (1) transforms data according to a step value and, (2) mines the transformed data for FTGPs.

Attributes

attr_cols
attr_size
col_count
data
display_patterns
display_patterns_as_df
full_attr_data
gradual_patterns
max_step
min_rep
row_count
target_col
thd_supp
time_cols
titles
valid_bins
warping_set