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docs/nbpages/quickstart/0200_simple_anomaly_detection.py
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| """ | ||
| Simple Anomaly Detection | ||
| ======================== | ||
| You can create and evaluate an anomaly detection model with just a few lines of code. | ||
| Provide your timeseries as a pandas dataframe with timestamp and value. | ||
| Optionally, you can also provide the anomaly labels as a column in the dataframe. | ||
| For example, to detect anomalies in daily sessions data, your dataframe could look like this: | ||
| .. code-block:: python | ||
| import pandas as pd | ||
| df = pd.DataFrame({ | ||
| "date": ["2020-01-08-00", "2020-01-09-00", "2020-01-10-00"], | ||
| "sessions": [10231.0, 12309.0, 12104.0], | ||
| "is_anomaly": [False, True, False] | ||
| }) | ||
| The time column can be any format recognized by `pandas.to_datetime`. | ||
| In this example, we'll load a dataset representing ``log(daily page views)`` | ||
| on the Wikipedia page for Peyton Manning. | ||
| It contains values from 2007-12-10 to 2016-01-20. More dataset info | ||
| `here <https://facebook.github.io/prophet/docs/quick_start.html>`_. | ||
| """ | ||
|
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||
| import warnings | ||
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| import plotly | ||
| from greykite.common.data_loader import DataLoader | ||
| from greykite.detection.detector.config import ADConfig | ||
| from greykite.detection.detector.data import DetectorData | ||
| from greykite.detection.detector.greykite import GreykiteDetector | ||
| from greykite.framework.templates.autogen.forecast_config import ForecastConfig | ||
| from greykite.framework.templates.autogen.forecast_config import MetadataParam | ||
| from greykite.framework.templates.model_templates import ModelTemplateEnum | ||
|
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| warnings.filterwarnings("ignore") | ||
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| # Loads dataset into pandas DataFrame | ||
| dl = DataLoader() | ||
| df = dl.load_peyton_manning() | ||
|
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| # specify dataset information | ||
| metadata = MetadataParam( | ||
| time_col="ts", # name of the time column ("date" in example above) | ||
| value_col="y", # name of the value column ("sessions" in example above) | ||
| freq="D" # "H" for hourly, "D" for daily, "W" for weekly, etc. | ||
| # Any format accepted by `pandas.date_range` | ||
| ) | ||
|
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| # %% | ||
| # Create an Anomaly Detection Model | ||
| # ------------------------------- | ||
| # Similar to forecasting, you need to provide a forecast config and an | ||
| # anomaly detection config. You can choose any of the available forecast model | ||
| # templates (see :doc:`/pages/stepbystep/0100_choose_model`). | ||
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| # In this example, we choose the "AUTO" model template for the forecast config, | ||
| # and the default anomaly detection config. | ||
| # The Silverkite "AUTO" model template chooses the parameter configuration | ||
| # given the input data frequency, forecast horizon and evaluation configs. | ||
|
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| anomaly_detector = GreykiteDetector() # Creates an instance of the Greykite anomaly detector | ||
|
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| forecast_config = ForecastConfig( | ||
| model_template=ModelTemplateEnum.AUTO.name, | ||
| forecast_horizon=7, # forecasts 7 steps ahead | ||
| coverage=None, # Confidence Interval will be tuned by the AD model | ||
| metadata_param=metadata) | ||
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| ad_config = ADConfig() # Default anomaly detection config | ||
|
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| detector = GreykiteDetector( | ||
| forecast_config=forecast_config, | ||
| ad_config=ad_config, | ||
| reward=None) | ||
|
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| # %% | ||
| # Train the Anomaly Detection Model | ||
| # --------------------------------- | ||
| # You can train the anomaly detection model by calling the ``fit`` method. | ||
| # This method takes a ``DetectorData`` object as input. | ||
| # The ``DetectorData`` object consists the time series information as a pandas dataframe. | ||
| # Optionally, you can also provide the anomaly labels as a column in the dataframe. | ||
| # The anomaly labels can also be provided as a list of boolean values. | ||
| # The anomaly labels are used to evaluate the model performance. | ||
|
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| train_size = int(2700) | ||
| df_train = df[:train_size].reset_index(drop=True) | ||
| train_data = DetectorData(df=df_train) | ||
| detector.fit(data=train_data) | ||
|
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| # %% | ||
| # Predict with the Anomaly Detection Model | ||
| # --------------------------------------- | ||
| # You can predict anomalies by calling the ``predict`` method. | ||
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| test_data = DetectorData(df=df) | ||
| test_data = detector.predict(test_data) | ||
|
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| # %% | ||
| # Evaluate the Anomaly Detection Model | ||
| # ------------------------------------ | ||
| # The output of the anomaly detection model are stored as attributes | ||
| # of the ``GreykiteDetector`` object. | ||
| # (The interactive plots are generated by ``plotly``: **click to zoom!**) | ||
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| # %% | ||
| # Training | ||
| # ^^^^^^^^ | ||
| # The ``fitted_df`` attribute contains the result on the training data. | ||
| # You can plot the result by calling the ``plot`` method with ``phase="train"``. | ||
| print(detector.fitted_df) | ||
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| fig = detector.plot( | ||
| phase="train", | ||
| title="Greykite Detector Peyton Manning - fit phase") | ||
| plotly.io.show(fig) | ||
|
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| # %% | ||
| # Prediction | ||
| # ^^^^^^^^^^ | ||
| # The ``pred_df`` attribute contains the predicted result. | ||
| # You can plot the result by calling the ``plot`` method with ``phase="predict"``. | ||
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| print(detector.pred_df) | ||
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| fig = detector.plot( | ||
| phase="predict", | ||
| title="Greykite Detector Peyton Manning - predict phase") | ||
| plotly.io.show(fig) | ||
|
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| # %% | ||
| # Model Summary | ||
| # ^^^^^^^^^^^^^^^^^ | ||
| # Model summary allows inspection of individual model terms. | ||
| # Check parameter estimates and their significance for insights | ||
| # on how the model works and what can be further improved. | ||
| # You can call the ``summary`` method to see the model summary. | ||
| summary = detector.summary() | ||
| print(summary) | ||
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| # %% | ||
| # What's next? | ||
| # ------------ | ||
| # If you're satisfied with the forecast performance, you're done! | ||
| # | ||
| # For a complete example of how to tune this forecast, see | ||
| # :doc:`/gallery/tutorials/0400_anomaly_detection_tutorial`. |
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