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arules.py
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arules.py
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# Module: Association Rules Mining
# Author: Moez Ali <[email protected]>
# License: MIT
def setup(data,
transaction_id,
item_id,
ignore_items = None,
session_id = None):
"""
Description:
------------
This function initializes the environment in pycaret. setup() must called before
executing any other function in pycaret. It takes three mandatory parameters:
(i) dataframe {array-like, sparse matrix}, (ii) transaction_id param identifying
basket and (iii) item_id param used to create rules. These three params are
normally found in any transactional dataset. pycaret will internally convert the
dataframe into a sparse matrix which is required for association rules mining.
Example
-------
from pycaret.datasets import get_data
france get_data('france')
experiment_name = setup(data = data, transaction_id = 'InvoiceNo',
item_id = 'ProductName')
Parameters
----------
data : {array-like, sparse matrix}, shape (n_samples, n_features) where n_samples
is the number of samples and n_features is the number of features.
transaction_id: string
Name of column representing transaction id. This will be used to pivot the matrix.
item_id: string
Name of column used for creation of rules. Normally, this will be the variable of
interest.
ignore_items: list, default = None
list of strings to be ignored when considering rule mining.
session_id: int, default = None
If None, a random seed is generated and returned in the Information grid. The
unique number is then distributed as a seed in all functions used during the
experiment. This can be used for later reproducibility of the entire experiment.
Returns:
--------
info grid: Information grid is printed.
-----------
environment: This function returns various outputs that are stored in variable
----------- as tuple. They are used by other functions in pycaret.
Warnings:
---------
None
"""
#exception checking
import sys
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#load dependencies
import random
import pandas as pd
import numpy as np
from IPython.display import display, HTML, clear_output, update_display
global X, txid, iid, ignore_list, seed, experiment__
#create an empty list for pickling later.
experiment__ = []
#storing items in variable
X = data
txid = transaction_id
iid = item_id
ignore_list = ignore_items
#generate seed to be used globally
if session_id is None:
seed = random.randint(150,9000)
else:
seed = session_id
#display info grid
#transactions
tx_unique = len(data[transaction_id].unique())
item_unique = len(data[item_id].unique())
if ignore_items is None:
ignore_flag = 'None'
else:
ignore_flag = ignore_items
functions = pd.DataFrame ( [ ['session_id', seed ],
['# Transactions', tx_unique ],
['# Items', item_unique ],
['Ignore Items', ignore_flag ],
], columns = ['Description', 'Value'] )
functions_ = functions.style.hide_index()
display(functions_)
return X, txid, iid, ignore_list, seed, experiment__
def create_model(metric='confidence',
threshold = 0.5,
min_support = 0.05,
round = 4):
"""
Description:
------------
This function creates an association rules model using data and identifiers
passed at setup stage. This function internally transforms the data for
association rule mining.
setup() function must be called before using create_model()
Example
-------
from pycaret.datasets import get_data
france get_data('france')
experiment_name = setup(data = data, transaction_id = 'InvoiceNo',
item_id = 'ProductName')
This will return dataframe containing rules sorted by metric param.
Parameters
----------
metric : string, default = 'confidence'
Metric to evaluate if a rule is of interest. Default is set to confidence.
Other available metrics include 'support', 'lift', 'leverage', 'conviction'.
These metrics are computed as follows:
- support(A->C) = support(A+C) [aka 'support'], range: [0, 1]
- confidence(A->C) = support(A+C) / support(A), range: [0, 1]
- lift(A->C) = confidence(A->C) / support(C), range: [0, inf]
- leverage(A->C) = support(A->C) - support(A)*support(C),
range: [-1, 1]
- conviction = [1 - support(C)] / [1 - confidence(A->C)],
range: [0, inf]
threshold : float, default = 0.5
Minimal threshold for the evaluation metric, via the `metric` parameter,
to decide whether a candidate rule is of interest.
min_support : float, default = 0.05
A float between 0 and 1 for minumum support of the itemsets returned.
The support is computed as the fraction `transactions_where_item(s)_occur /
total_transactions`.
round: integer, default = 4
Number of decimal places metrics in score grid will be rounded to.
Returns:
--------
DataFrame: Dataframe containing rules of interest with all metrics
--------- including antecedents, consequents, antecedent support,
consequent support, support, confidence, lift, leverage,
conviction.
Warnings:
---------
- Setting low values for min_support may increase training time.
"""
#loading dependencies
import pandas as pd
from IPython.display import display, HTML, clear_output, update_display
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
#reshaping the dataframe
basket = X.groupby([txid, iid])[iid].count().unstack().reset_index().fillna(0).set_index(txid)
if ignore_list is not None:
basket = basket.drop(ignore_list, axis=1)
def encode_units(x):
if x <= 0:
return 0
if x >= 1:
return 1
basket = basket.applymap(encode_units)
frequent_itemsets = apriori(basket, min_support=min_support, use_colnames=True)
rules = association_rules(frequent_itemsets, metric=metric, min_threshold=threshold)
rules = rules.sort_values(by=[metric],ascending=False).reset_index(drop=True)
rules = rules.round(round)
#storing into experiment
tup = ('Basket',basket)
experiment__.append(tup)
tup = ('Frequent Itemsets',frequent_itemsets)
experiment__.append(tup)
tup = ('Rules',rules)
experiment__.append(tup)
return(rules)
def plot_model(model,
plot = '2d'):
"""
Description:
------------
This function takes a model dataframe returned by create_model() function.
'2d' and '3d' plots are available.
Example:
--------
rule1 = create_model(metric='confidence', threshold=0.7, min_support=0.05)
plot_model(rule1, plot='2d')
plot_model(rule1, plot='3d')
Parameters
----------
model : DataFrame, default = none
DataFrame returned by trained model using create_model().
plot : string, default = '2d'
Enter abbreviation of type of plot. The current list of plots supported are:
Name Abbreviated String
--------- ------------------
Support, Confidence and Lift (2d) '2d'
Support, Confidence and Lift (3d) '3d'
Returns:
--------
Visual Plot: Prints the visual plot.
------------
Warnings:
---------
"""
#loading libraries
import numpy as np
import pandas as pd
import plotly.express as px
from IPython.display import display, HTML, clear_output, update_display
#import cufflinks
import cufflinks as cf
cf.go_offline()
cf.set_config_file(offline=False, world_readable=True)
#copy dataframe
data_ = model.copy()
antecedents = []
for i in data_['antecedents']:
i = str(i)
a = i.split(sep="'")
a = a[1]
antecedents.append(a)
data_['antecedents'] = antecedents
antecedents_short = []
for i in antecedents:
a = i[:10]
antecedents_short.append(a)
data_['antecedents_short'] = antecedents_short
consequents = []
for i in data_['consequents']:
i = str(i)
a = i.split(sep="'")
a = a[1]
consequents.append(a)
data_['consequents'] = consequents
if plot == '2d':
fig = px.scatter(data_, x="support", y="confidence", text="antecedents_short", log_x=True, size_max=600, color='lift',
hover_data = ['antecedents', 'consequents'], opacity=0.5, )
fig.update_traces(textposition='top center')
fig.update_layout(plot_bgcolor='rgb(240,240,240)')
fig.update_layout(
height=800,
title_text='2D Plot of Support, Confidence and Lift'
)
fig.show()
if plot == '3d':
fig = px.scatter_3d(data_, x='support', y='confidence', z='lift',
color='antecedent support', title='3d Plot for Rule Mining', opacity=0.7, width=900, height=800,
hover_data = ['antecedents', 'consequents' ])
fig.show()
def get_rules(data,
transaction_id,
item_id,
ignore_items = None,
metric='confidence',
threshold = 0.5,
min_support = 0.05):
"""
Magic function to get Association Rules in Power Query / Power BI.
"""
s = setup(data=data, transaction_id=transaction_id, item_id=item_id, ignore_items = ignore_items)
dataset = create_model(metric=metric, threshold=threshold, min_support=min_support, round=4)
return dataset