Metadata-Version: 2.1
Name: pyanomaly
Version: 0.0.1
Summary: A description of your project
Home-page: https://github.com/fillipedem/pyanomaly
Author: Fillipe
Author-email: fillipedem@gmail.com
License: Apache Software License 2.0
Description: # pyanomaly
        > Conjunto de algoritmos para detectar anomalias em Series Temporais.
        
        
        ## Instalação
        
        pip install pyanomaly
        
        ## Como usar
        
        Iremos realizar os testes no dataset contendo temperaturas diarias da cidade de Melbourne.
        
        dataset: https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv
        
        ```python
        # data
        import numpy as np
        import pandas as pd
        # plot
        import matplotlib.pyplot as plt
        import seaborn as sns; sns.set()
        
        df = pd.read_csv('./dados/daily-min-temperatures.csv', parse_dates=['Date'])
        df.set_index('Date', inplace=True)
        ```
        
        ```python
        df.head(5).T
        ```
        
        
        
        
        <div>
        <style scoped>
            .dataframe tbody tr th:only-of-type {
                vertical-align: middle;
            }
        
            .dataframe tbody tr th {
                vertical-align: top;
            }
        
            .dataframe thead th {
                text-align: right;
            }
        </style>
        <table border="1" class="dataframe">
          <thead>
            <tr style="text-align: right;">
              <th>Date</th>
              <th>1981-01-01</th>
              <th>1981-01-02</th>
              <th>1981-01-03</th>
              <th>1981-01-04</th>
              <th>1981-01-05</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <th>Temp</th>
              <td>20.7</td>
              <td>17.9</td>
              <td>18.8</td>
              <td>14.6</td>
              <td>15.8</td>
            </tr>
          </tbody>
        </table>
        </div>
        
        
        
        ```python
        df.plot(figsize=(8, 4));
        ```
        
        
        ![png](docs/images/output_6_0.png)
        
        
        ## Mad
        
        ```python
        mad = MAD()
        mad.fit(df['Temp'])
        outliers = mad.fit_predict(df['Temp'])
        
        outliers.head()
        ```
        
        
        
        
            Date
            1981-01-15    25.0
            1981-01-18    24.8
            1981-02-09    25.0
            1982-01-17    24.0
            1982-01-20    25.2
            Name: Temp, dtype: float64
        
        
        
        ```python
        fig, ax = plt.subplots(1, 1, figsize=(12, 6))
                               
        sns.lineplot(x=df.index, y=df['Temp'], ax=ax)
        sns.scatterplot(x=outliers.index, y=outliers, 
                        color='r', ax=ax)
        
        plt.title('Zscore Robusto', fontsize='large');
        ```
        
        
        ![png](docs/images/output_9_0.png)
        
        
        ## Tukey
        
        ```python
        tu = Tukey()
        
        tu.fit(df['Temp'])
        outliers = tu.predict(df['Temp'])
        
        outliers.head()
        ```
        
        
        
        
            Date
            1981-01-15    25.0
            1981-01-18    24.8
            1981-02-09    25.0
            1982-01-17    24.0
            1982-01-20    25.2
            Name: Temp, dtype: float64
        
        
        
        ```python
        fig, ax = plt.subplots(1, 1, figsize=(12, 6))
                               
        sns.lineplot(x=df.index, y=df['Temp'], ax=ax)
        sns.scatterplot(x=outliers.index, y=outliers, 
                        color='r', ax=ax)
        
        plt.title('Tukey Method', fontsize='large');
        ```
        
        
        ![png](docs/images/output_12_0.png)
        
        
        ## Twitter - S-MAD
        
        ```python
        outliers = twitter(df['Temp'], period=12)
        outliers.head()
        ```
        
        
        
        
            Date
            1981-01-15    25.0
            1981-01-18    24.8
            1981-02-09    25.0
            1982-01-20    25.2
            1982-02-15    26.3
            Name: Temp, dtype: float64
        
        
        
        ```python
        fig, ax = plt.subplots(1, 1, figsize=(12, 6))
                               
        sns.lineplot(x=df.index, y=df['Temp'], ax=ax)
        sns.scatterplot(x=outliers.index, y=outliers, 
                        color='r', ax=ax)
        
        plt.title('Tukey Method', fontsize='large');
        ```
        
        
        ![png](docs/images/output_15_0.png)
        
        
Keywords: python anomaly detection outlier
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
