Merge pull request #35 from zStupan/main

Update docs, Update dependencies, add methods to RuleList
firefly-cpp · Mar 14, 2022 · 534ed0a · 534ed0a
2 parents ad99667 + 14f367e
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diff --git a/docs/getting_started.rst b/docs/getting_started.rst
@@ -6,16 +6,195 @@ This section is going to show you how to use the NiaARM framework.
 Installation
 ------------
 
-Firstly, install NiaARM package using the following command:
+You can install NiaARM package using the following command:
 
 .. code:: bash
 
     pip install niaarm
 
+Usage
+-----
+
+Loading Data
+~~~~~~~~~~~~
+
+In NiaARM, data loading is done via the :class:`~niaarm.dataset.Dataset` class.
+There are two options for loading data:
+
+**Option 1: Directly from file**
+
+.. code:: python
+
+    from niaarm import Dataset
+
+    dataset = Dataset('Abalone.csv')
+    print(dataset)
+
+**Option 2: From a pandas DataFrame (recommended)**
+
+This option is recommended, as it allows you to preprocess the data before mining.
+
+.. code:: python
+
+    import pandas as pd
+    from niaarm import Dataset
+
+
+    df = pd.read_csv('Abalone.csv')
+    # Preprocess the dataframe...
+    dataset = Dataset(df)
+    print(dataset)
+
+**Output:**
+
+.. code:: text
+
+    DATASET INFO:
+    Number of transactions: 4177
+    Number of features: 9
+
+    FEATURE INFO:
+
+                        Sex Length Diameter Height Whole weight Shucked weight Viscera weight Shell weight Rings
+    dtype       categorical  float    float  float        float          float          float        float   int
+    min_val             N/A  0.075    0.055    0.0        0.002          0.001         0.0005       0.0015     1
+    max_val             N/A  0.815     0.65   1.13       2.8255          1.488           0.76        1.005    29
+    categories    [M, F, I]    N/A      N/A    N/A          N/A            N/A            N/A          N/A   N/A
+
+Mining Association Rules
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+Once the data has been loaded, we can run our mining algorithm.
+
+The key component here is our :class:`~niaarm.niaarm.NiaARM` class, which inherits from NiaPy's
+Problem class. It implements numerical association rule mining as a real valued, single
+objective, unconstrained maximization problem (more details on this approach can be found
+`here <https://link.springer.com/chapter/10.1007/978-3-030-68154-8_19>`__ and
+`here <http://www.iztok-jr-fister.eu/static/publications/231.pdf>`__).
+To summarize, for each solution vector a :class:`~niaarm.rule.Rule` is built,
+and it's fitness is computed as a weighted sum of selected interest measures (metrics).
+The rule is then appended to a list of rules, which can be accessed through the NiaARM class.
+
+The :class:`~niaarm.niaarm.NiaARM` class takes the dataset's
+dimension (calculated dimension of the optimization problem), features, and transactions
+(all attributes of the :class:`~niaarm.dataset.Dataset` class) and the metrics selected for
+the fitness function. The metrics can either be passed in as a sequence of strings, in
+which case the weights of the metrics will be set to 1, or you can pass in a dict containing
+pairs of ``{'metric_name': weight}``. You can also enable logging of fitness improvements
+by setting the ``logging`` parameter to ``True``.
+
+Bellow is a simple example of mining association rules on the Abalone dataset that we
+loaded above. For this example we picked Differential Evolution, specifically DE/rand/1/bin,
+which we'll be running for 50 iterations.
+All available algorithms can be found in the `NiaPy documentation <https://niapy.org/en/stable/>`__.
+We've selected the metrics: 'support', 'confidence', 'inclusion' and 'amplitude' for the fitness
+function. We then sort the rules by fitness in descending order and export them to csv.
+
+.. code:: python
+
+    from niaarm import NiaARM
+    from niapy.task import OptimizationType, Task
+    from niapy.algorithms.basic import DifferentialEvolution
+
+
+    # DE/rand/1/bin
+    algorithm = DifferentialEvolution(population_size=50,
+                                      differential_weight=0.8,
+                                      crossover_probability=0.9)
+
+    metrics = ('support', 'confidence', 'inclusion', 'amplitude')
+
+    problem = NiaARM(dataset.dimension, dataset.features, dataset.transactions, metrics, logging=True)
+    task = Task(problem, max_iters=50, optimization_type=OptimizationType.MAXIMIZATION)
+
+    algorithm.run(task)
+
+    problem.rules.sort(by='fitness', reverse=True)
+    problem.rules.to_csv('output.csv')
+
+The mined rules are stored in ``problem.rules``, a :class:`~niaarm.rule_list.RuleList`. A
+RuleList is a thin wrapper around a normal python list, with the added functionalities of
+sorting by metric, exporting rules to csv, and properties for getting statistical data
+about the rules. Printing a RuleList prints a statistical report of the rules in it.
+
+**Output:**
+
+.. code:: text
+
+    Fitness: 0.4421065111459649, Support: 0.00023940627244433804, Confidence: 1.0, Inclusion: 0.3333333333333333, Amplitude: 0.43485330497808217
+    Fitness: 0.5363319939110781, Support: 0.006942781900885803, Confidence: 0.9354838709677419, Inclusion: 0.5555555555555556, Amplitude: 0.6473457672201293
+    Fitness: 0.5395969006117709, Support: 0.1812305482403639, Confidence: 0.9895424836601308, Inclusion: 0.4444444444444444, Amplitude: 0.5431701261021447
+    Fitness: 0.5560783231641568, Support: 0.0023940627244433805, Confidence: 1.0, Inclusion: 0.6666666666666666, Amplitude: 0.5552525632655172
+    Fitness: 0.5711107256845077, Support: 0.5997127124730668, Confidence: 1.0, Inclusion: 0.3333333333333333, Amplitude: 0.3513968569316307
+    Fitness: 0.5970815767218225, Support: 0.8099114196791956, Confidence: 0.9955856386109476, Inclusion: 0.3333333333333333, Amplitude: 0.2494959152638132
+    Fitness: 0.6479501714015481, Support: 0.7455111323916687, Confidence: 0.9860671310956302, Inclusion: 0.3333333333333333, Amplitude: 0.5268890887855602
+    Fitness: 0.6497709183879634, Support: 0.9820445295666747, Confidence: 1.0, Inclusion: 0.4444444444444444, Amplitude: 0.17259469954073503
+    Fitness: 0.6522418829904134, Support: 0.9176442422791478, Confidence: 0.9422320550639135, Inclusion: 0.4444444444444444, Amplitude: 0.304646790174148
+    Fitness: 0.6600433108204055, Support: 0.9762987790280105, Confidence: 1.0, Inclusion: 0.5555555555555556, Amplitude: 0.1083189086980556
+    Fitness: 0.6625114159138297, Support: 0.9209959300933684, Confidence: 1.0, Inclusion: 0.3333333333333333, Amplitude: 0.39571640022861654
+    Fitness: 0.6748446186051374, Support: 0.9916207804644481, Confidence: 0.9916207804644481, Inclusion: 0.4444444444444444, Amplitude: 0.27169246904720923
+    Fitness: 0.6868285539707781, Support: 0.949006463969356, Confidence: 0.9927372902579514, Inclusion: 0.5555555555555556, Amplitude: 0.25001490610024923
+    Rules exported to output.csv
+
+
+Mining Association Rules (Simplified)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In addition to the above interface, we provide a much simpler one in the form of a simple
+function: :class:`~niaarm.mine.get_rules`. The function accepts a dataset object, an algorithm,
+sequence or dict of metrics, a stopping condition (either ``max_evals`` or ``max_iters``) and
+a ``logging`` flag. The algorithm can either be a NiaPy Algorithm instance, or a string,
+in which case it's parameters can be passed in to the function as additional keyword arguments.
+
+The :class:`~niaarm.mine.get_rules` function returns a named tuple of (rules, run_time),
+where rules is a :class:`~niaarm.rule_list.RuleList` and run_time is the run time of
+the algorithm in seconds.
+
+The same example as above, using :class:`~niaarm.mine.get_rules`:
+
+.. code:: python
+
+    from niaarm import get_rules
+    from niapy.algorithms.basic import DifferentialEvolution
+
+
+    # DE/rand/1/bin
+    algorithm = DifferentialEvolution(population_size=50,
+                                      differential_weight=0.8,
+                                      crossover_probability=0.9)
+
+    metrics = ('support', 'confidence', 'inclusion', 'amplitude')
+    rules, run_time = get_rules(dataset, algorithm, metrics, max_iters=50)
+    print(rules)
+    print(f'Run Time: {run_time:.4f} seconds')
+    rules.to_csv('output.csv')
+
+**Output:**
+
+.. code:: text
+
+    STATS:
+    Total rules: 1153
+    Average fitness: 0.47320577312454054
+    Average support: 0.3983325861836626
+    Average confidence: 0.7050696319555724
+    Average lift: 1.8269022321777044
+    Average coverage: 0.5791478590164908
+    Average consequent support: 0.6708142990119975
+    Average conviction: 80294763647830.92
+    Average amplitude: 0.33832710930158877
+    Average inclusion: 0.45109376505733834
+    Average interestingness: 0.4107718184209992
+    Average comprehensibility: 0.6225319999993354
+    Average netconf: 0.08165217509315073
+    Average Yule's Q: 0.2631267094311884
+    Average length of antecedent: 2.248048568950564
+    Average length of consequent: 1.8117953165654814
+    Run Time: 6.9498 seconds
+    Rules exported to output.csv
 
-After the successful installation you are ready to run your first example.
 
 Examples
 --------
 
-You can find usage examples `here <https://github.com/firefly-cpp/NiaARM/tree/main/examples>`_.
+You can find the full code and usage examples `here <https://github.com/firefly-cpp/NiaARM/tree/main/examples>`_.
diff --git a/niaarm/rule_list.py b/niaarm/rule_list.py
@@ -36,6 +36,54 @@ def sort(self, by='fitness', reverse=True):
         """
         self.data.sort(key=lambda rule: getattr(rule, by), reverse=reverse)
 
+    def mean(self, metric):
+        """Get mean value of metric.
+
+        Args:
+            metric (str): Metric.
+
+        Returns:
+            float: Mean value of metric in rule list.
+
+        """
+        return np.mean([getattr(rule, metric) for rule in self.data])
+
+    def min(self, metric):
+        """Get min value of metric.
+
+        Args:
+            metric (str): Metric.
+
+        Returns:
+            float: Min value of metric in rule list.
+
+        """
+        return min(self.data, key=lambda x: getattr(x, metric))
+
+    def max(self, metric):
+        """Get max value of metric.
+
+        Args:
+            metric (str): Metric.
+
+        Returns:
+            float: Max value of metric in rule list.
+
+        """
+        return max(self.data, key=lambda x: getattr(x, metric))
+
+    def std(self, metric):
+        """Get standard deviation of metric.
+
+        Args:
+            metric (str): Metric.
+
+        Returns:
+            float: Standard deviation of metric in rule list.
+
+        """
+        return np.std([getattr(rule, metric) for rule in self.data])
+
     def to_csv(self, filename):
         """Export rules to csv.
 

diff --git a/poetry.lock b/poetry.lock
diff --git a/pyproject.toml b/pyproject.toml
@@ -14,7 +14,7 @@ python = "^3.7"
 niapy = "^2.0.1"
 numpy = [
     { version = "^1.21.5", python = ">=3.7,<3.11" },
-    { version = "^1.22.0", python = "^3.11" }
+    { version = "^1.22.3", python = "^3.11" }
 ]
 pandas = [
     { version = "^1.3.5", python = ">=3.7.1,<3.8" },