/
__init__.py
183 lines (155 loc) · 6.17 KB
/
__init__.py
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"""
Python 3 Wrapper for SPMF
http://www.philippe-fournier-viger.com/spmf
Inspiration from:
https://github.com/fandu/maximal-sequential-patterns-mining
http://forum.ai-directory.com/read.php?5,5510
"""
__author__ = "Lorenz Leitner"
__version__ = "1.4"
__license__ = "GNU GPL v3.0"
import pandas as pd
import os
import subprocess
import tempfile
class Spmf:
def __init__(self,
algorithm_name,
input_direct=None,
input_type="normal",
input_filename="",
output_filename="spmf-output.txt",
arguments=[],
spmf_bin_location_dir=".",
memory=0):
self.executable_dir_ = spmf_bin_location_dir
self.executable_ = "spmf.jar"
self.is_exist_executable_ = os.path.isfile(
os.path.join(self.executable_dir_, self.executable_))
if not self.is_exist_executable_:
self.executable_dir_ = os.path.dirname(os.path.realpath(__file__))
self.is_exist_executable_ = os.path.isfile(
os.path.join(self.executable_dir_, self.executable_))
if not self.is_exist_executable_:
raise FileNotFoundError(self.executable_ + " not found. Please" +
" use the spmf_bin_location_dir argument.")
self.agorithm_name_ = algorithm_name
self.input_ = self.handle_input(
input_direct, input_filename, input_type)
self.output_ = output_filename
self.arguments_ = [str(a) for a in arguments]
self.patterns_ = []
self.df_ = None
self.memory_ = memory
def handle_input(self, input_direct, input_filename, input_type):
if input_filename:
return input_filename
if type(input_direct) == str:
if input_type == "normal":
file_ending = ".txt"
elif input_type == "text":
file_ending = ".text"
return self.write_temp_input_file(input_direct,
file_ending)
if type(input_direct) == list:
if input_type == "normal":
seq_spmf = ""
for seq in input_direct:
for item_set in seq:
for item in item_set:
seq_spmf += str(item) + ' '
seq_spmf += str(-1) + ' '
seq_spmf += str(-2) + '\n'
return self.write_temp_input_file(seq_spmf, ".txt")
if input_type == "text":
seq_str = ""
for seq in input_direct:
seq_str += seq + ". "
return self.write_temp_input_file(seq_str, ".text")
raise TypeError("no correct input format found (required: " +
"list or str, or input file via" +
" input_filename parameter)")
def write_temp_input_file(self, input_text, file_ending):
tf = tempfile.NamedTemporaryFile(delete=False)
tf.write(bytes(input_text, 'UTF-8'))
name = tf.name
os.rename(name, name + file_ending)
return name + file_ending
def run(self):
"""
Start the SPMF process
Calls the Java binary with the previously specified parameters
"""
subprocess_arguments = ["java"]
# http://www.philippe-fournier-viger.com/spmf/index.php?link=FAQ.php#memory
if self.memory_:
subprocess_arguments.append(f'-Xmx{self.memory_}m')
subprocess_arguments.extend(
["-jar",
os.path.join(self.executable_dir_, self.executable_),
"run",
self.agorithm_name_,
self.input_, self.output_])
subprocess_arguments.extend(self.arguments_)
proc = subprocess.check_output(subprocess_arguments)
proc_output = proc.decode()
print(proc_output)
if "java.lang.IllegalArgumentException" in proc_output:
raise TypeError("java.lang.IllegalArgumentException")
def parse_output(self):
"""
Parse the output of SPMF and saves in in member variable patterns_
-1 separates itemsets
-2 indicates end of a sequence
http://data-mining.philippe-fournier-viger.com/introduction-to-sequential-rule-mining/#comment-4114
"""
lines = []
with open(self.output_, "r") as f:
lines = f.readlines()
patterns = []
for line in lines:
line = line.strip()
patterns.append(line.split(" -1 "))
self.patterns_ = patterns
return patterns
def to_pandas_dataframe(self, pickle=False):
"""
Convert output to pandas DataFrame
pickle: Save as serialized pickle
"""
# TODO: Optional parameter for pickle file name
if not self.patterns_:
self.parse_output()
patterns_dict_list = []
for pattern_sup in self.patterns_:
pattern = pattern_sup[:-1]
sup = pattern_sup[-1:][0]
sup = sup.strip()
if not sup.startswith("#SUP"):
print("support extraction failed")
sup = sup.split()
sup = sup[1]
patterns_dict_list.append({'pattern': pattern, 'sup': int(sup)})
df = pd.DataFrame(patterns_dict_list)
self.df_ = df
if pickle:
df.to_pickle(self.output_.replace(".txt", ".pkl"))
return df
def to_csv(self, file_name, df=None, list_as_string=True):
"""
Save output as csv
Either use member variable if it has already been set,
or re-set it using to_pandas_dataframe, or use given dataframe
list_as_string: Fix CSV output so that '[]' is not present
"""
if self.df_ is None and df is None:
self.df_ = self.to_pandas_dataframe()
if df is not None:
self.df_ = df
if not list_as_string:
self.df_.to_csv(file_name, sep=';', index=False)
else:
df = self.df_
for _, row in df.iterrows():
row['pattern'] = ','.join(row['pattern'])
df.to_csv(file_name, sep=';', index=False)