Installation

Install the current PyPI release:
```
$ pip install permetrics==1.4.3
```

Install directly from source code:

$ git clone https://github.com/thieu1995/permetrics.git
$ cd permetrics
$ python setup.py install

In case, you want to install the development version from Github:
```
$ pip install git+https://github.com/thieu1995/permetrics
```

After installation, you can import Permetrics as any other Python module:

$ python
>>> import permetrics
>>> permetrics.__version__

Let’s go through some examples.

Examples

Functional Style

This is traditional way to call a specific metric you want to use. Everytime you want to use a function, you need to pass y_true and y_pred

## 1. Import packages, classes
## 2. Create object
## 3. From object call function and use

import numpy as np
from permetrics import RegressionMetric

y_true = np.array([3, -0.5, 2, 7, 5, 6])
y_pred = np.array([2.5, 0.0, 2, 8, 5, 6])

evaluator = RegressionMetric()

## 3.1 Call specific function inside object, each function has 2 names like below
rmse_1 = evaluator.RMSE(y_true, y_pred)
rmse_2 = evaluator.root_mean_squared_error(y_true, y_pred)
print(f"RMSE: {rmse_1}, {rmse_2}")

mse = evaluator.MSE(y_true, y_pred)
mae = evaluator.MAE(y_true, y_pred, decimal=5)
print(f"MSE: {mse}, MAE: {mae}")

import numpy as np
from permetrics import ClassificationMetric

y_true = [0, 1, 0, 0, 1, 0]
y_pred = [0, 1, 0, 0, 0, 1]

evaluator = ClassificationMetric()

ps1 = evaluator.precision_score(y_true, y_pred, decimal=5)
ps2 = evaluator.PS(y_true, y_pred, decimal=3)
ps3 = evaluator.PS(y_true, y_pred, decimal=4)
print(f"Precision: {ps1}, {ps2}, {ps3}")

recall = evaluator.recall_score(y_true, y_pred)
accuracy = evaluator.accuracy_score(y_true, y_pred)
print(f"recall: {recall}, accuracy: {accuracy}")

import numpy as np
from permetrics import ClusteringMetric

y_true = [0, 1, 0, 0, 1, 0]
y_pred = [0, 1, 0, 0, 0, 1]

evaluator = ClusteringMetric()

ps1 = evaluator.mutual_info_score(y_true, y_pred, decimal=5)
ps2 = evaluator.MIS(y_true, y_pred, decimal=3)
print(f"Mutual Information score: {ps1}, {ps2}")

homogeneity = evaluator.homogeneity_score(y_true, y_pred)
completeness  = evaluator.CS(y_true, y_pred)
print(f"Homogeneity: {homogeneity}, Completeness : {completeness}")

OOP Style

This is modern and better way to use metrics. You only need to pass y_true, y_pred one time when creating metric object.
After that, you can get the value of any metrics without passing y_true, y_pred

## 1. Import packages, classes
## 2. Create object
## 3. From object call function and use

import numpy as np
from permetrics import RegressionMetric

y_true = np.array([3, -0.5, 2, 7, 5, 6])
y_pred = np.array([2.5, 0.0, 2, 8, 5, 6])

evaluator = RegressionMetric(y_true, y_pred, decimal=5)

## Get the result of any function you want to
rmse = evaluator.RMSE()
mse = evaluator.MSE()
mae = evaluator.MAE()

print(f"RMSE: {rmse}, MSE: {mse}, MAE: {mae}")

import numpy as np
from permetrics import ClassificationMetric

y_true = [0, 1, 0, 0, 1, 0]
y_pred = [0, 1, 0, 0, 0, 1]

evaluator = ClassificationMetric(y_true, y_pred, decimal=5)

## Get the result of any function you want to

hamming_score = evaluator.hamming_score()
mcc = evaluator.matthews_correlation_coefficient()
specificity = evaluator.specificity_score()
print(f"HL: {hamming_score}, MCC: {mcc}, specificity: {specificity}")

import numpy as np
from permetrics import ClusteringMetric

y_true = [0, 1, 0, 0, 1, 0]
y_pred = [0, 1, 0, 0, 0, 1]

evaluator = ClusteringMetric(y_true, y_pred, decimal=5)

## Get the result of any function you want to

x1 = evaluator.kulczynski_score()
x2 = evaluator.mc_nemar_score()
x3 = evaluator.rogers_tanimoto_score()
print(f"Kulczynski: {x1}, Mc Nemar: {x2}, Rogers Tanimoto: {x3}")

Multiple Metrics

To reduce coding time when using multiple metrics. There are few ways to do it with permetrics by using OOP style

import numpy as np
from permetrics import RegressionMetric

y_true = np.array([3, -0.5, 2, 7, 5, 6])
y_pred = np.array([2.5, 0.0, 2, 8, 5, 6])

evaluator = RegressionMetric(y_true, y_pred, decimal=5)

## Define list of metrics you want to use
list_metrics = ["RMSE", "MAE", "MAPE", "NSE"]

## 1. Get list metrics by using loop
list_results = []
for metric in list_metrics:
        list_results.append( evaluator.get_metric_by_name(metric) )
print(list_results)


## 2. Get list metrics by using function
dict_result_2 = evaluator.get_metrics_by_list_names(list_metrics)
print(dict_result_2)


## 3. Get list metrics by using function and parameters
dict_metrics = {
    "RMSE": {"decimal": 5},
    "MAE": {"decimal": 4},
    "MAPE": None,
    "NSE": {"decimal": 3},
}
dict_result_3 = evaluator.get_metrics_by_dict(dict_metrics)
print(dict_result_3)

import numpy as np
from permetrics import ClassificationMetric

y_true = [0, 1, 0, 0, 1, 0]
y_pred = [0, 1, 0, 0, 0, 1]

evaluator = ClassificationMetric(y_true, y_pred, decimal=5)

## 1. Get list metrics by using loop
list_metrics = ["PS", "RS", "LS", "SS"]
list_results = []
for metric in list_metrics:
    list_results.append( evaluator.get_metric_by_name(metric) )
print(list_results)

## 2. Get list metrics by using function
dict_result_2 = evaluator.get_metrics_by_list_names(list_metrics)
print(dict_result_2)

## 3. Get list metrics by using function and parameters
dict_metrics = {
    "PS": {"average": "micro"},
    "RS": {"average": "macro"},
    "LS": None,
    "SS": {"average": "weighted"},
}
dict_result_3 = evaluator.get_metrics_by_dict(dict_metrics)
print(dict_result_3)

Multiple Outputs Multiple Metrics

Scikit-learn library is limited with multi-output metrics, but permetrics can produce multi-output for all of metrics

import numpy as np
from permetrics import RegressionMetric

## This y_true and y_pred have 4 columns, 4 outputs
y_true = np.array([ [3, -0.5, 2, 7],
                    [5, 6, -0.3, 9],
                    [-11, 23, 8, 3.9] ])

y_pred = np.array([ [2.5, 0.0, 2, 8],
                    [5.2, 5.4, 0, 9.1],
                    [-10, 23, 8.2, 4] ])

evaluator = RegressionMetric(y_true, y_pred, decimal=5)

## 1. By default, all metrics can automatically return the multi-output results
# rmse = evaluator.RMSE()
# print(rmse)

## 2. If you want to take mean of all outputs, can set the parameter: multi-output = "mean"
# rmse_2 = evaluator.RMSE(multi_output="mean")
# print(rmse_2)

## 3. If you want a specific metric has more important than other, you can set weight for each output.
# rmse_3 = evaluator.RMSE(multi_output=[0.5, 0.05, 0.1, 0.35])
# print(rmse_3)


## Get multiple metrics with multi-output or single-output by parameters


## 1. Get list metrics by using list_names
list_metrics = ["RMSE", "MAE", "MSE"]
list_paras = [
    {"decimal": 3, "multi_output": "mean"},
    {"decimal": 4, "multi_output": [0.5, 0.2, 0.1, 0.2]},
    {"decimal": 5, "multi_output": "raw_values"}
]
dict_result_1 = evaluator.get_metrics_by_list_names(list_metrics, list_paras)
print(dict_result_1)


## 2. Get list metrics by using dict_metrics
dict_metrics = {
    "RMSE": {"decimal": 5, "multi_output": "mean"},
    "MAE": {"decimal": 4, "multi_output": "raw_values"},
    "MSE": {"decimal": 2, "multi_output": [0.5, 0.2, 0.1, 0.2]},
}
dict_result_2 = evaluator.get_metrics_by_dict(dict_metrics)
print(dict_result_2)

For more usage examples please look at [examples](/examples) folder.