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ImpactLearning-1.8
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مشاهده بیشترتوضیحات
-
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | ImpactLearning-1.8 |
| نام | ImpactLearning |
| نسخه کتابخانه | 1.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Kowsher Ahmed, Avishek Das |
| ایمیل نویسنده | ahmedshuvo969@gmail.com, avishek.das.ayan@gmail.com |
| آدرس صفحه اصلی | https://github.com/Kowsher/Impact-Learning- |
| آدرس اینترنتی | https://pypi.org/project/ImpactLearning/ |
| مجوز | MIT |
# Impact Learning
## Impact Learning is a new machine learning algoirthm.
Impact learning is a supervised and competitive learning algorithm for inducing classification, linear or polynomial regression knowledge from examples. The primary principle of this method is to learn from a competition which is the impact of independent features; to be more specific it fits curve by the back forces or impacts of features from the intrinsic rate of natural increase (RNI); since every real dataset follows the aptitude of RNI. The input to Impact Learning is a training set of numerical data.
To be more prominently, every feature of our life follows the trend of RNI, on the other hand, there are more back forces on which the feature need to be dependent. As a result, the target is impacted by other features of the back forces which can be named for a specific force as “Back Impact on Target (BIT)”. Since the target feature relies on BITs that is why every BIT also depends on the target feature.
Basically, the machine learning or statistical learning datasets derive from real sectors of target territories, consequently, they flow the trend of RNI. So it will be a procedure to generate the algorithm (Impact Learning) from the flow of RNI. Furthermore, this method learns from the effect of BITs and in real life, every business sector has good competitors; the impact learning can be used in order to depict the competition among the competitors. In addition, the trained impact learning can be also used for checking multicollinearity or redundancy for feature selection.
-A framework of this algorithm is being developed. Very soon, it will be made open source, if you have captivating to use in your work just [email me](ga.kowsher@gamil.com)
## Installation:
```
pip install ImpactLearning
```
## Usage of Regressor:
### 1. validation with test data
```python
from ImpactLearning import Regressor
import numpy as np
import pandas as pd
dataTrain = pd.read_csv('brainhead_train.csv')
dataTest = pd.read_csv('brainhead_test.csv')
x_train = dataTrain.iloc[:, :-1].values
y_train = dataTrain.iloc[:, 3].values
x_test = dataTest.iloc[:, :-1].values
y_test = dataTest.iloc[:, 3].values
il = Regressor()
il.fit(x_train,y_train, x_test, y_test, loss_function="MAE", optimizer = "GD",)
il.train(epochs=2000, lr=0.5, progress_per=100)
```
Output:
```
Epoch: 100, train_loss: 741.998779, test_loss: 659.725098
Epoch: 200, train_loss: 67.431602, test_loss: 54.413006
---------------------------------------------------
Epoch: 2000, train_loss: 66.067902, test_loss: 52.447800
Training Completed
```
#### 1.1 Get Scores
```python
il.get_scores()
```
Output
```
{'max_test_loss': 4119.4272,
'max_train_loss': 4534.379,
'min_test_loss': 50.623547,
'min_train_loss': 66.0679}
```
### 2. without validation
```python
from ImpactLearning import Regressor
import numpy as np
import pandas as pd
dataTrain = pd.read_csv('brainhead_train.csv')
dataTest = pd.read_csv('brainhead_test.csv')
x_train = dataTrain.iloc[:, :-1].values
y_train = dataTrain.iloc[:, 3].values
x_test = dataTest.iloc[:, :-1].values
y_test = dataTest.iloc[:, 3].values
il = Regressor()
il.fit(x_train,y_train, loss_function="MAE", optimizer = "GD",)
il.train(epochs=2000, lr=0.5, progress_per=100)
```
Output:
```
Epoch: 100, loss: 741.998779
Epoch: 200, loss: 67.431602
----------------------
Epoch: 2000, loss: 66.067902
```
#### 2.1 Get Scores
```python
il.get_scores()
```
Output
```
Loss: 66.067902
```
### 3. Prediction
```python
il.predict(x_test[:5])
```
Output
```
array([[1303.717 ],
[1108.7083],
[1177.2151],
[1048.9008],
[1297.9176]], dtype=float32)
```
### 4. Coefficients
```python
il.get_coefficients()
```
Output
```
{'Bias': array([-38.508266], dtype=float32),
'Carrying Capacity': 1635.1,
'RNI': array([-1.0669531], dtype=float32),
'WeightX': array([[ -0.34179878],
[ 0.5568241 ],
[-288.52682 ]], dtype=float32),
'Weighty': array([796.0016], dtype=float32)}
```
### 5. Loss Values
```python
il.getLossValues()
```
Output
```
[4534.379,
4418.933,
4309.73,
4206.1646,
4107.7134,
----------]
```
## Usage of Classifier:
### 1. validation with test data
```python
from ImpactLearning import Classifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris=load_iris()
X=iris.data
y=iris.target
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
il = Classifier()
il.fit(x_train, y_train,x_test, y_test, CCapacity=1, num_classes=3, optimizer="GD", loss_function="CategoricalCrossentropy")
il.train(epochs = 1000, lr=0.001, progress_per=100)
```
Output:
```
Epoch: 100, train_loss: 0.484322, test_loss: 0.479262, train_accuracy: 0.683333 test_accuracy: 0.666667
Epoch: 200, train_loss: 0.376250, test_loss: 0.355702, train_accuracy: 0.775000 test_accuracy: 0.800000
----------------------------------------------------
Epoch: 1000, train_loss: 0.055509, test_loss: 0.148505, train_accuracy: 0.975000 test_accuracy: 0.966667
Training Completed
```
#### 1.1 Get Scores
```python
il.get_scores()
```
Output
```
{'max_test_accuracy': 0.96666664,
'max_test_loss': 2.5907264,
'max_train_accuracy': 0.98333335,
'max_train_loss': 2.04851,
'min_test_accuracy': 0.16666667,
'min_test_loss': 0.11300503,
'min_train_accuracy': 0.34166667,
'min_train_loss': 0.0509217}
```
### 2. without validation
```python
from ImpactLearning import Classifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris=load_iris()
X=iris.data
y=iris.target
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
il = Classifier()
il.fit(x_train, y_train,CCapacity=1, num_classes=3, optimizer="GD", loss_function="CategoricalCrossentropy")
il.train(epochs = 1000, lr=0.001, progress_per=100)
```
Output:
```
Epoch: 100, loss: 0.456280, train_accuracy: 0.716667
Epoch: 200, loss: 0.356472, train_accuracy: 0.808333
-----------------------------------------
Epoch: 1000, loss: 0.100295, train_accuracy: 0.966667
Training Completed
```
#### 2.1 Get Scores
```python
il.get_scores()
```
Output
```
Accuracy: 0.966667
```
### 3. Prediction
```python
il.predict(x_test[:10])
```
Output
```
array([1, 2, 0, 1, 2, 0, 2, 1, 0, 1])
```
### 4. Coefficients
```python
il.get_coefficients()
```
Output
```
{'Bias': array([ 0.00366024, 0.00528361, -0.00894384], dtype=float32),
'Carrying Capacity': 1.1,
'RNI': 1.0128337,
'WeightX': array([[0.90882355, 0.91059417, 1.1805835 ],
[0.80056006, 0.9750843 , 1.2243568 ],
[1.2586796 , 1.0555391 , 0.68578094],
[1.1205307 , 1.1224369 , 0.7570329 ]], dtype=float32),
'Weighty': 0.98588395}
```
### 5. Loss Values
```python
il.getLossValues()
```
Output
```
[1.0501021,
1.0416512,
----------
0.08514832,
0.082014434,
0.07910187]
```
## Loss Functions
```
FOR Regressor
1.logcosh
2.huber
3.MSE
4.MAE
5.MAPE
6.Poisson
7.sqr_hinge
FOR Classifier
1.BinaryCrossentropy
2.CategoricalCrossentropy
3.CosineSimilarity
4.Hinge
5.CategoricalHinge
6.Logosh
7.Poisson
8.SquaredHinge
9.KLD
```
## OPTIMIZERS with supported Arguments
use the arguments in fit() method
```
Adadelta
rho=0.95, epsilon=1e-07
Adagrad
initial_accumulator_value=0.1, epsilon=1e-07
Adam
beta_1=0.9, beta_2=0.999, epsilon=1e-07
Adamax
beta_1=0.9, beta_2=0.999, epsilon=1e-07
Ftrl
learning_rate_power=-0.5, initial_accumulator_value=0.1,
l1_regularization_strength=0.0, l2_regularization_strength=0.0,
l2_shrinkage_regularization_strength=0.0
Nadam
beta_1=0.9, beta_2=0.999, epsilon=1e-07
RMSprop
rho=0.9, momentum=0.0, epsilon=1e-07
SGD
momentum=0.0, nesterov=False, name='SGD'
GD
No Args
```
for more info about the arguments, visit https://www.tensorflow.orgapi_docs/python/tf/keras/optimizers
نیازمندی
| مقدار | نام |
|---|---|
| ==2.10.0 | h5py |
| ==2.2.0 | tensorflow |
| ==1.18.5 | numpy |
نحوه نصب
نصب پکیج whl ImpactLearning-1.8:
pip install ImpactLearning-1.8.whl
نصب پکیج tar.gz ImpactLearning-1.8:
pip install ImpactLearning-1.8.tar.gz