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.gitignore

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+# Initially taken from Github's Python gitignore file
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
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+
+# tests and logs
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+
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+MANIFEST
+
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+
+# Installer logs
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+# .lock
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+# DS_Store (MacOS)
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LICENSE

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+Copyright 2022 Philipp J. Rösch
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

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+# Multilabel Oversampling
+
+**Many algorithms for imbalanced data support binary and multiclass classification only.**
+**This approach is made for mulit-label classification (aka multi-target classification).**
+
+
+
+## :slot_machine: Algorithm
+
+* Multilabel dataset (as `pandas.DataFrame`) with imbalanced data
+* Calculate counts per class and then calculate the standard deviation (std) of the count values
+* Do for `number_of_adds` times the following: 
+* Randomly draw a sample from your data and calculate new std 
+ * If new std reduces, add sample to your dataset
+ * If not, draw another sample (to this up to `number_of_tries` times)
+* A new df is returned. 
+* A result plot viszualize the target distribition before and after upsampling. Moreover the counts per index are shown. 
+
+## :arrow_right: Usage
+
+```python
+from multilabel_oversampling import multilabel_oversampling as mo
+
+df = mo.create_fake_data(size=1, seed=3)
+ml_oversampler = mo.MultilabelOversampler(number_of_adds=100, number_of_tries=100)
+df_new = ml_oversampler.fit(df)
+#> Iteration: 20%|██████ | 20/100 [00:00<00:00, 111.68it/s]
+#> No improvement after 100 tries in iter 20.
+```
+![Plot from df_new = ml_oversampler.fit(df)](assets/plot.png)
+
+```python
+ml_oversampler.plot_results()
+```
+
+![Plot from ml_oversampler.plot_results()](assets/plot_results.png)
+
+## :information_source: Install
+
+* Install from GitHub
+
+```bash
+pip install git+https://github.com/phiyodr/multilabel-oversampling
+```
+
+:sunflower:

multilabel_oversampling/__init__.py

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+from .multilabel_oversampling import *

multilabel_oversampling/multilabel_oversampling.py

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+import numpy as np
+import pandas as pd
+from sklearn.utils import shuffle
+import copy
+import seaborn as sns
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+import random
+import os
+import math
+
+
+def seed_everything(seed=1):
+ """"
+ Seed everything.
+ """ 
+ random.seed(seed)
+ np.random.seed(seed)
+
+def create_fake_data(size=1, seed=1):
+ seed_everything(seed)
+ y1 = np.concatenate((np.ones(16*size), np.zeros(4*size))).astype(int)
+ y2 = np.concatenate((np.ones(12*size), np.zeros(8*size))).astype(int)
+ y3 = shuffle(np.concatenate((np.ones(4*size), np.zeros(16*size)))).astype(int)
+ y4 = shuffle(np.concatenate((np.ones(4*size), np.zeros(16*size)))).astype(int)
+ size = 20* size
+ x = [f"img_{x}.jpg" for x in range(size)]
+ df = pd.DataFrame({"y1": y1, "y2": y2, "y3": y3, "y4": y4, "x": x})
+ return df
+
+class MultilabelOversampler:
+
+ def __init__(self, number_of_adds=1000, number_of_tries=100, tqdm_disable=False, details=False, plot=True):
+ """
+
+ 
+ Args:
+ number_of_add: Maximum number of new rows add to df. Total number of iterations.
+ number_of_tries: Maximum number of draws from df within total number of iterations.
+ tqdm_disable: Enable progress bar for each iteration.
+ details: Enable detailed feedback for each try
+ plot: Plot all tries (iteration vs. std) after process is finished.
+ """
+ if number_of_adds:
+ self.number_of_adds = number_of_adds
+ else:
+ self.number_of_adds = 1e6
+ if number_of_tries:
+ self.number_of_tries = number_of_tries
+ else:
+ self.number_of_tries = 1e6
+
+ self.tqdm_disable = tqdm_disable
+ self.details = details
+ self.plot = plot
+
+
+ def fit(self, df, target_list=["y1", "y2", "y3", "y4"]):
+ """
+
+ Args:
+ df: Unbalanced DataFrame
+ target_list: List of target variables. All other variables are treated as explanatory variables. 
+ """
+ self.reset()
+ self.target_list = target_list
+ self.df = copy.deepcopy(df)
+ df_new = copy.deepcopy(df)
+ res_std = []
+ res_bad = []
+
+
+ for iter_ in tqdm(range(self.number_of_adds),desc="Iteration", disable=self.tqdm_disable):
+ current_std = df_new[self.target_list].sum().std()
+
+ # Take random row and add to df_new
+ not_working = []
+ for try_ in tqdm(range(self.number_of_tries), desc=f"Iter {iter_}", disable=True):
+ random_row = df.sample(n = 1)
+ df_interim = pd.concat((df_new, random_row))
+ new_std = df_interim[self.target_list].sum().std()
+ # If std improves add row, otherwise add to not_working list
+ if new_std < current_std:
+ df_new = df_interim
+ res_std.append(new_std)
+ if self.details:
+ print(f"Iter {iter_:3}: Worked after {try_:5} tries with row {random_row.index[0]:4}, Std: {current_std:.3f}, New: {new_std:.3f}, Shape: {df_new.shape}", flush=True)
+ break
+ else:
+ not_working.append((random_row.index[0], new_std))
+ if (try_+1) == self.number_of_tries:
+ print(f"No improvement after {self.number_of_tries} tries in iter {iter_}.")
+ break
+ res_bad.append(not_working)
+ #plt.plot(res_std)
+ #plt.show()
+ #df_new.sum().plot.bar()
+ self.df_new = df_new
+ self.res_std = res_std
+ self.res_bad = res_bad
+ if (len(res_std) > 0) and self.plot:
+ self.plot_all_tries(self.res_std, self.res_bad)
+ plt.show()
+ return df_new
+
+ def reset(self):
+ self.target_list = None
+ self.df = None
+ self.df_new = None
+ self.res_std = None
+ self.res_bad = None
+
+ @staticmethod
+ def plot_all_tries(res_std, res_bad):
+ y_max = max([x[1] for x in res_bad[0]]) * 1.1
+ plt.plot(res_std)
+ plt.scatter(range(len(res_std)), res_std)
+ plt.ylim(0, y_max)
+ for i, row_std in enumerate(res_bad):
+ for idx, (j, s) in enumerate(row_std):
+ #plt.text(i + idx*0.02, s, f"{j}", fontsize=8)
+ plt.scatter(i + idx*0.01, s)
+ plt.xlabel('Iters')#, fontsize=18)
+ plt.ylabel('Std')#, fontsize=16)
+
+ def plot_results(self):
+ plt.subplot(2,2,1)
+ self.plot_distr(self.df, "before")
+ plt.subplot(2,2,2)
+ self.plot_distr(self.df_new, "after")
+ plt.subplot(2,2,(3,4)) # MatplotlibDeprecationWarning
+ self.plot_index_counts(self.df_new)
+ plt.tight_layout()
+ plt.show()
+
+ def plot_distr(self, df, when):
+ df[self.target_list].sum().plot.bar()
+ plt.title(f"Label distribution \n{when} upsampling")
+ return plt
+
+ def plot_index_counts(self, df_new):
+ """TODO make better xticks alignment"""
+ idxs = list(df_new.index)
+ lens = len(set(idxs))
+ plt.hist(idxs, bins=lens, width=.1)#, edgecolor='k')
+ xint = range(min(idxs), math.ceil(max(idxs))+1)
+ plt.xticks(xint)
+ plt.title("Draws per index\n in new df")
+ return plt
+
+if __name__ == '__main__':
+ df = create_fake_data(size=1, seed=3)
+ print(df)
+ mlo = MultilabelOversampling(number_of_adds=100)
+ df_new = mlo.fit(df)
+ mlo.plot_results()

requirements.txt

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+numpy
+scikit-learn
+pandas
+seaborn
+tqdm
+matplotlib