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autogen/website/docs/Examples/AutoML-Time series forecast.md
Chi Wang e877de6414 use ffill in forecasting example
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AutoML - Time Series Forecast

Prerequisites

Install the [ts_forecast] option.

pip install "flaml[ts_forecast]"

Simple NumPy Example

import numpy as np
from flaml import AutoML

X_train = np.arange('2014-01', '2022-01', dtype='datetime64[M]')
y_train = np.random.random(size=84)
automl = AutoML()
automl.fit(X_train=X_train[:84],  # a single column of timestamp
           y_train=y_train,  # value for each timestamp
           period=12,  # time horizon to forecast, e.g., 12 months
           task='ts_forecast', time_budget=15,  # time budget in seconds
           log_file_name="ts_forecast.log",
           eval_method="holdout",
          )
print(automl.predict(X_train[84:]))

Sample output

[flaml.automl: 01-21 08:01:20] {2018} INFO - task = ts_forecast
[flaml.automl: 01-21 08:01:20] {2020} INFO - Data split method: time
[flaml.automl: 01-21 08:01:20] {2024} INFO - Evaluation method: holdout
[flaml.automl: 01-21 08:01:20] {2124} INFO - Minimizing error metric: mape
[flaml.automl: 01-21 08:01:21] {2181} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax']
[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 0, current learner lgbm
[flaml.automl: 01-21 08:01:21] {2547} INFO - Estimated sufficient time budget=1429s. Estimated necessary time budget=1s.
[flaml.automl: 01-21 08:01:21] {2594} INFO -  at 0.9s,  estimator lgbm's best error=0.9811,     best estimator lgbm's best error=0.9811
[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 1, current learner lgbm
[flaml.automl: 01-21 08:01:21] {2594} INFO -  at 0.9s,  estimator lgbm's best error=0.9811,     best estimator lgbm's best error=0.9811
[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 2, current learner lgbm
[flaml.automl: 01-21 08:01:21] {2594} INFO -  at 0.9s,  estimator lgbm's best error=0.9811,     best estimator lgbm's best error=0.9811
[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 3, current learner lgbm
[flaml.automl: 01-21 08:01:21] {2594} INFO -  at 1.0s,  estimator lgbm's best error=0.9811,     best estimator lgbm's best error=0.9811
[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 4, current learner lgbm
[flaml.automl: 01-21 08:01:21] {2594} INFO -  at 1.0s,  estimator lgbm's best error=0.9811,     best estimator lgbm's best error=0.9811
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[flaml.automl: 01-21 08:01:21] {2594} INFO -  at 1.0s,  estimator lgbm's best error=0.9652,     best estimator lgbm's best error=0.9652
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[flaml.automl: 01-21 08:01:21] {2594} INFO -  at 1.0s,  estimator lgbm's best error=0.9466,     best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2594} INFO -  at 1.1s,  estimator lgbm's best error=0.9466,     best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 18, current learner rf
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[flaml.automl: 01-21 08:01:27] {2594} INFO -  at 6.6s,  estimator prophet's best error=1.4076,  best estimator arima's best error=0.5693
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Univariate time series

import statsmodels.api as sm

data = sm.datasets.co2.load_pandas().data
# data is given in weeks, but the task is to predict monthly, so use monthly averages instead
data = data['co2'].resample('MS').mean()
data = data.bfill().ffill()  # makes sure there are no missing values
data = data.to_frame().reset_index()
num_samples = data.shape[0]
time_horizon = 12
split_idx = num_samples - time_horizon
train_df = data[:split_idx]  # train_df is a dataframe with two columns: timestamp and label
X_test = data[split_idx:]['index'].to_frame()  # X_test is a dataframe with dates for prediction
y_test = data[split_idx:]['co2']  # y_test is a series of the values corresponding to the dates for prediction

from flaml import AutoML

automl = AutoML()
settings = {
    "time_budget": 10,  # total running time in seconds
    "metric": 'mape',  # primary metric for validation: 'mape' is generally used for forecast tasks
    "task": 'ts_forecast',  # task type
    "log_file_name": 'CO2_forecast.log',  # flaml log file
    "eval_method": "holdout",  # validation method can be chosen from ['auto', 'holdout', 'cv']
    "seed": 7654321,  # random seed
}

automl.fit(dataframe=train_df,  # training data
           label='co2',  # label column
           period=time_horizon,  # key word argument 'period' must be included for forecast task)
           **settings)

Sample output

[flaml.automl: 01-21 07:54:04] {2018} INFO - task = ts_forecast
[flaml.automl: 01-21 07:54:04] {2020} INFO - Data split method: time
[flaml.automl: 01-21 07:54:04] {2024} INFO - Evaluation method: holdout
[flaml.automl: 01-21 07:54:04] {2124} INFO - Minimizing error metric: mape
Importing plotly failed. Interactive plots will not work.
[flaml.automl: 01-21 07:54:04] {2181} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax']
[flaml.automl: 01-21 07:54:04] {2434} INFO - iteration 0, current learner lgbm
[flaml.automl: 01-21 07:54:05] {2547} INFO - Estimated sufficient time budget=2145s. Estimated necessary time budget=2s.
[flaml.automl: 01-21 07:54:05] {2594} INFO -  at 0.9s,  estimator lgbm's best error=0.0621,     best estimator lgbm's best error=0.0621
[flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 1, current learner lgbm
[flaml.automl: 01-21 07:54:05] {2594} INFO -  at 1.0s,  estimator lgbm's best error=0.0574,     best estimator lgbm's best error=0.0574
[flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 2, current learner lgbm
[flaml.automl: 01-21 07:54:05] {2594} INFO -  at 1.0s,  estimator lgbm's best error=0.0464,     best estimator lgbm's best error=0.0464
[flaml.automl: 01-21 07:54:05] {2434} INFO - iteration 3, current learner lgbm
[flaml.automl: 01-21 07:54:05] {2594} INFO -  at 1.0s,  estimator lgbm's best error=0.0464,     best estimator lgbm's best error=0.0464
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[flaml.automl: 01-21 07:54:05] {2594} INFO -  at 1.0s,  estimator lgbm's best error=0.0365,     best estimator lgbm's best error=0.0365
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[flaml.automl: 01-21 07:54:05] {2594} INFO -  at 1.1s,  estimator lgbm's best error=0.0192,     best estimator lgbm's best error=0.0192
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[flaml.automl: 01-21 07:54:05] {2594} INFO -  at 1.2s,  estimator lgbm's best error=0.0036,     best estimator lgbm's best error=0.0036
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[flaml.automl: 01-21 07:54:05] {2594} INFO -  at 1.4s,  estimator lgbm's best error=0.0023,     best estimator lgbm's best error=0.0023
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[flaml.automl: 01-21 07:54:06] {2434} INFO - iteration 20, current learner rf
[flaml.automl: 01-21 07:54:06] {2594} INFO -  at 2.1s,  estimator rf's best error=0.0228,       best estimator lgbm's best error=0.0017
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[flaml.automl: 01-21 07:54:06] {2594} INFO -  at 2.2s,  estimator xgboost's best error=0.6738,  best estimator lgbm's best error=0.0017
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[flaml.automl: 01-21 07:54:06] {2594} INFO -  at 2.3s,  estimator xgboost's best error=0.0242,  best estimator lgbm's best error=0.0017
[flaml.automl: 01-21 07:54:06] {2434} INFO - iteration 28, current learner extra_tree
[flaml.automl: 01-21 07:54:06] {2594} INFO -  at 2.4s,  estimator extra_tree's best error=0.0245,       best estimator lgbm's best error=0.0017
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[flaml.automl: 01-21 07:54:06] {2594} INFO -  at 2.6s,  estimator rf's best error=0.0210,       best estimator lgbm's best error=0.0017
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[flaml.automl: 01-21 07:54:06] {2594} INFO -  at 2.8s,  estimator extra_tree's best error=0.0158,       best estimator lgbm's best error=0.0017
[flaml.automl: 01-21 07:54:06] {2434} INFO - iteration 36, current learner xgb_limitdepth
[flaml.automl: 01-21 07:54:07] {2594} INFO -  at 2.8s,  estimator xgb_limitdepth's best error=0.0447,   best estimator lgbm's best error=0.0017
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[flaml.automl: 01-21 07:54:07] {2594} INFO -  at 2.9s,  estimator xgb_limitdepth's best error=0.0029,   best estimator lgbm's best error=0.0017
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[flaml.automl: 01-21 07:54:09] {2594} INFO -  at 5.5s,  estimator prophet's best error=0.0008,  best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:10] {2594} INFO -  at 6.1s,  estimator arima's best error=0.0047,    best estimator prophet's best error=0.0008
[flaml.automl: 01-21 07:54:10] {2434} INFO - iteration 45, current learner sarimax
[flaml.automl: 01-21 07:54:10] {2594} INFO -  at 6.4s,  estimator sarimax's best error=0.0047,  best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:11] {2594} INFO -  at 6.9s,  estimator arima's best error=0.0047,    best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:11] {2594} INFO -  at 7.6s,  estimator xgboost's best error=0.0242,  best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:13] {2594} INFO -  at 9.3s,  estimator prophet's best error=0.0005,  best estimator prophet's best error=0.0005
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[flaml.automl: 01-21 07:54:14] {2434} INFO - iteration 58, current learner rf
[flaml.automl: 01-21 07:54:14] {2594} INFO -  at 10.0s, estimator rf's best error=0.0146,       best estimator prophet's best error=0.0005
[flaml.automl: 01-21 07:54:14] {2824} INFO - retrain prophet for 0.6s
[flaml.automl: 01-21 07:54:14] {2831} INFO - retrained model: <prophet.forecaster.Prophet object at 0x7fb68ea65d60>
[flaml.automl: 01-21 07:54:14] {2210} INFO - fit succeeded
[flaml.automl: 01-21 07:54:14] {2211} INFO - Time taken to find the best model: 9.339771270751953
[flaml.automl: 01-21 07:54:14] {2222} WARNING - Time taken to find the best model is 93% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.

Compute and plot predictions

The example plotting code requires matplotlib.

flaml_y_pred = automl.predict(X_test)
import matplotlib.pyplot as plt

plt.plot(X_test, y_test, label='Actual level')
plt.plot(X_test, flaml_y_pred, label='FLAML forecast')
plt.xlabel('Date')
plt.ylabel('CO2 Levels')
plt.legend()

png

Multivariate Time Series (Forecasting with Exogeneous Variables)

import pandas as pd

# pd.set_option("display.max_rows", None, "display.max_columns", None)
multi_df = pd.read_csv(
    "https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/nyc_energy_consumption.csv"
)

# preprocessing data
multi_df["timeStamp"] = pd.to_datetime(multi_df["timeStamp"])
multi_df = multi_df.set_index("timeStamp")
multi_df = multi_df.resample("D").mean()
multi_df["temp"] = multi_df["temp"].fillna(method="ffill")
multi_df["precip"] = multi_df["precip"].fillna(method="ffill")
multi_df = multi_df[:-2]  # last two rows are NaN for 'demand' column so remove them
multi_df = multi_df.reset_index()

# Using temperature values create categorical values
# where 1 denotes daily tempurature is above monthly average and 0 is below.
def get_monthly_avg(data):
    data["month"] = data["timeStamp"].dt.month
    data = data[["month", "temp"]].groupby("month")
    data = data.agg({"temp": "mean"})
    return data

monthly_avg = get_monthly_avg(multi_df).to_dict().get("temp")

def above_monthly_avg(date, temp):
    month = date.month
    if temp > monthly_avg.get(month):
        return 1
    else:
        return 0

multi_df["temp_above_monthly_avg"] = multi_df.apply(
    lambda x: above_monthly_avg(x["timeStamp"], x["temp"]), axis=1
)

del multi_df["temp"], multi_df["month"]  # remove temperature column to reduce redundancy

# split data into train and test
num_samples = multi_df.shape[0]
multi_time_horizon = 180
split_idx = num_samples - multi_time_horizon
multi_train_df = multi_df[:split_idx]
multi_test_df = multi_df[split_idx:]

multi_X_test = multi_test_df[
    ["timeStamp", "precip", "temp_above_monthly_avg"]
]  # test dataframe must contain values for the regressors / multivariate variables
multi_y_test = multi_test_df["demand"]

# initialize AutoML instance
automl = AutoML()

# configure AutoML settings
settings = {
    "time_budget": 10,  # total running time in seconds
    "metric": "mape",  # primary metric
    "task": "ts_forecast",  # task type
    "log_file_name": "energy_forecast_categorical.log",  # flaml log file
    "eval_method": "holdout",
    "log_type": "all",
    "label": "demand",
}

# train the model
automl.fit(dataframe=df, **settings, period=time_horizon)

# predictions
print(automl.predict(multi_X_test))

Sample Output

[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 15, current learner xgboost
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.2s,	estimator xgboost's best error=0.0959,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 16, current learner extra_tree
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.2s,	estimator extra_tree's best error=0.0961,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 17, current learner extra_tree
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.2s,	estimator extra_tree's best error=0.0961,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 18, current learner xgboost
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.2s,	estimator xgboost's best error=0.0959,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 19, current learner xgb_limitdepth
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.3s,	estimator xgb_limitdepth's best error=0.0820,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 20, current learner xgboost
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.3s,	estimator xgboost's best error=0.0834,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 21, current learner xgb_limitdepth
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.4s,	estimator xgb_limitdepth's best error=0.0820,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 22, current learner lgbm
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.4s,	estimator lgbm's best error=0.0925,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 23, current learner xgb_limitdepth
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.4s,	estimator xgb_limitdepth's best error=0.0820,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 24, current learner extra_tree
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.5s,	estimator extra_tree's best error=0.0922,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 25, current learner xgb_limitdepth
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.5s,	estimator xgb_limitdepth's best error=0.0820,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 26, current learner rf
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.5s,	estimator rf's best error=0.0862,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 27, current learner rf
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.6s,	estimator rf's best error=0.0856,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 28, current learner xgb_limitdepth
[flaml.automl: 02-28 21:32:26] {2620} INFO -  at 6.6s,	estimator xgb_limitdepth's best error=0.0820,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:27] {2458} INFO - iteration 29, current learner sarimax
[flaml.automl: 02-28 21:32:28] {2620} INFO -  at 7.9s,	estimator sarimax's best error=0.5313,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:28] {2458} INFO - iteration 30, current learner xgboost
[flaml.automl: 02-28 21:32:28] {2620} INFO -  at 8.0s,	estimator xgboost's best error=0.0834,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:28] {2458} INFO - iteration 31, current learner xgb_limitdepth
[flaml.automl: 02-28 21:32:28] {2620} INFO -  at 8.0s,	estimator xgb_limitdepth's best error=0.0791,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:28] {2458} INFO - iteration 32, current learner arima
[flaml.automl: 02-28 21:32:30] {2620} INFO -  at 10.3s,	estimator arima's best error=0.5998,	best estimator prophet's best error=0.0592
[flaml.automl: 02-28 21:32:32] {2850} INFO - retrain prophet for 2.2s
[flaml.automl: 02-28 21:32:32] {2857} INFO - retrained model: <prophet.forecaster.Prophet object at 0x000001B1D3EE2B80>
[flaml.automl: 02-28 21:32:32] {2234} INFO - fit succeeded
[flaml.automl: 02-28 21:32:32] {2235} INFO - Time taken to find the best model: 4.351356506347656

Forecasting Discrete Variables

from hcrystalball.utils import get_sales_data
import numpy as np
from flaml import AutoML

time_horizon = 30
df = get_sales_data(n_dates=180, n_assortments=1, n_states=1, n_stores=1)
df = df[["Sales", "Open", "Promo", "Promo2"]]

# feature engineering - create a discrete value column
# 1 denotes above mean and 0 denotes below mean
df["above_mean_sales"] = np.where(df["Sales"] > df["Sales"].mean(), 1, 0)
df.reset_index(inplace=True)

# train-test split
discrete_train_df = df[:-time_horizon]
discrete_test_df = df[-time_horizon:]
discrete_X_train, discrete_X_test = (
    discrete_train_df[["Date", "Open", "Promo", "Promo2"]],
    discrete_test_df[["Date", "Open", "Promo", "Promo2"]],
)
discrete_y_train, discrete_y_test = discrete_train_df["above_mean_sales"], discrete_test_df["above_mean_sales"]

# initialize AutoML instance
automl = AutoML()

# configure the settings
settings = {
    "time_budget": 15,  # total running time in seconds
    "metric": "accuracy",  # primary metric
    "task": "ts_forecast_classification",  # task type
    "log_file_name": "sales_classification_forecast.log",  # flaml log file
    "eval_method": "holdout",
}

# train the model
automl.fit(X_train=discrete_X_train,
           y_train=discrete_y_train,
           **settings,
           period=time_horizon)

# make predictions
discrete_y_pred = automl.predict(discrete_X_test)
print("Predicted label", discrete_y_pred)
print("True label", discrete_y_test)

Sample Output

[flaml.automl: 02-28 21:53:03] {2060} INFO - task = ts_forecast_classification
[flaml.automl: 02-28 21:53:03] {2062} INFO - Data split method: time
[flaml.automl: 02-28 21:53:03] {2066} INFO - Evaluation method: holdout
[flaml.automl: 02-28 21:53:03] {2147} INFO - Minimizing error metric: 1-accuracy
[flaml.automl: 02-28 21:53:03] {2205} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth']
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 0, current learner lgbm
[flaml.automl: 02-28 21:53:03] {2573} INFO - Estimated sufficient time budget=269s. Estimated necessary time budget=0s.
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.1s,	estimator lgbm's best error=0.2667,	best estimator lgbm's best error=0.2667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 1, current learner lgbm
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.1s,	estimator lgbm's best error=0.2667,	best estimator lgbm's best error=0.2667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 2, current learner lgbm
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.1s,	estimator lgbm's best error=0.1333,	best estimator lgbm's best error=0.1333
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 3, current learner rf
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.2s,	estimator rf's best error=0.1333,	best estimator lgbm's best error=0.1333
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 4, current learner xgboost
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.2s,	estimator xgboost's best error=0.1333,	best estimator lgbm's best error=0.1333
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 5, current learner lgbm
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.2s,	estimator lgbm's best error=0.1333,	best estimator lgbm's best error=0.1333
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 6, current learner rf
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.3s,	estimator rf's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 7, current learner lgbm
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.3s,	estimator lgbm's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 8, current learner lgbm
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.3s,	estimator lgbm's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 9, current learner lgbm
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.4s,	estimator lgbm's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 10, current learner rf
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.4s,	estimator rf's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 11, current learner rf
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.4s,	estimator rf's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 12, current learner xgboost
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.5s,	estimator xgboost's best error=0.1333,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 13, current learner extra_tree
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.5s,	estimator extra_tree's best error=0.1333,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 14, current learner xgb_limitdepth
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.5s,	estimator xgb_limitdepth's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 15, current learner xgboost
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.6s,	estimator xgboost's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 16, current learner xgb_limitdepth
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.6s,	estimator xgb_limitdepth's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 17, current learner rf
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.6s,	estimator rf's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 18, current learner xgb_limitdepth
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.7s,	estimator xgb_limitdepth's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 19, current learner lgbm
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.7s,	estimator lgbm's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 20, current learner extra_tree
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.7s,	estimator extra_tree's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 21, current learner xgboost
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.7s,	estimator xgboost's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 22, current learner extra_tree
[flaml.automl: 02-28 21:53:03] {2620} INFO -  at 0.8s,	estimator extra_tree's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 23, current learner rf
[flaml.automl: 02-28 21:53:04] {2620} INFO -  at 0.8s,	estimator rf's best error=0.0667,	best estimator rf's best error=0.0667
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 24, current learner xgboost
[flaml.automl: 02-28 21:53:04] {2620} INFO -  at 0.9s,	estimator xgboost's best error=0.0333,	best estimator xgboost's best error=0.0333
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 25, current learner xgb_limitdepth
[flaml.automl: 02-28 21:53:04] {2620} INFO -  at 0.9s,	estimator xgb_limitdepth's best error=0.0667,	best estimator xgboost's best error=0.0333
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 26, current learner xgb_limitdepth
[flaml.automl: 02-28 21:53:04] {2620} INFO -  at 0.9s,	estimator xgb_limitdepth's best error=0.0667,	best estimator xgboost's best error=0.0333
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 27, current learner xgboost
[flaml.automl: 02-28 21:53:04] {2620} INFO -  at 0.9s,	estimator xgboost's best error=0.0333,	best estimator xgboost's best error=0.0333
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 28, current learner extra_tree
[flaml.automl: 02-28 21:53:04] {2620} INFO -  at 1.0s,	estimator extra_tree's best error=0.0667,	best estimator xgboost's best error=0.0333
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 29, current learner xgb_limitdepth
[flaml.automl: 02-28 21:53:04] {2620} INFO -  at 1.0s,	estimator xgb_limitdepth's best error=0.0667,	best estimator xgboost's best error=0.0333
[flaml.automl: 02-28 21:53:04] {2850} INFO - retrain xgboost for 0.0s
[flaml.automl: 02-28 21:53:04] {2857} INFO - retrained model: XGBClassifier(base_score=0.5, booster='gbtree',
              colsample_bylevel=0.9826753651836615, colsample_bynode=1,
              colsample_bytree=0.9725493834064914, gamma=0, gpu_id=-1,
              grow_policy='lossguide', importance_type='gain',
              interaction_constraints='', learning_rate=0.1665803484560213,
              max_delta_step=0, max_depth=0, max_leaves=4,
              min_child_weight=0.5649012460525115, missing=nan,
              monotone_constraints='()', n_estimators=4, n_jobs=-1,
              num_parallel_tree=1, objective='binary:logistic', random_state=0,
              reg_alpha=0.009638363373006869, reg_lambda=0.143703802530408,
              scale_pos_weight=1, subsample=0.9643606787051899,
              tree_method='hist', use_label_encoder=False,
              validate_parameters=1, verbosity=0)
[flaml.automl: 02-28 21:53:04] {2234} INFO - fit succeeded
[flaml.automl: 02-28 21:53:04] {2235} INFO - Time taken to find the best model: 0.8547139167785645

Link to notebook | Open in colab