What is the expected input of IndividualAnomalyTransductive()? about group-anomaly-detection HOT 2 CLOSED

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime

from grand import IndividualAnomalyInductive, IndividualAnomalyTransductive, GroupAnomaly

df = pd.read_csv("simulated_data.csv", parse_dates=True, header = 0, index_col=0) # Added index_col=0
df.index = pd.to_datetime(df.index) # The timestamps are now our index column
df.columns = ["value"] # the columns (features) excluding the index

df.plot() # there is no column "timestamp" now, it's the index
plt.show()

model = IndividualAnomalyTransductive(ref_group = ["day-of-week"], w_martingale = 100)

# You can also try with "season-of-year" as the periodicity in your data seems seasonal
# model = IndividualAnomalyTransductive(ref_group = ["season-of-year"], w_martingale = 100)

for t, x in zip(df.index, df.values):
    info = model.predict(t, x)
    print("Time: {} ==> strangeness: {}, deviation: {}".format(t, info.strangeness, info.deviation), end="\r")

# Just added this line to see the results
model.plot_deviations(figsize=(12, 8), plots=["data", "strangeness", "deviation", "pvalue", "threshold"])

model = IndividualAnomalyTransductive(
            ref_group = ["day-of-week"] # Criteria to use to construct reference data (check the notebook examples to see other possible criteria to use).
            external_percentage = 0.3   # Percentage of samples to pick from historical data in the case where ref_group is set to "external".

            # The following parameters are the same as in IndividualAnomalyInductive
            non_conformity = "knn",     # Strangeness measure, e.g. "knn" or "median"
            k = 20                      # Used if non_conformity is "knn"
            w_martingale = 15,          # Window size used for computing the deviation level
            dev_threshold = 0.6,        # Threshold on the deviation level (in [0, 1])
            columns=None                # Optional feature names (for interpreting the results)
)