yujunjun/autogen
1
0
Fork 0
mirror of https://github.com/microsoft/autogen.git synced 2025-10-28 00:08:58 +00:00
Code Issues Packages Projects Releases Wiki Activity

update documentation for time series forecasting (#472)

Browse Source 
* update automl.py
- documentation update

* update test_forecast.py

* update model.py

* update automl_time_series_forecast.ipynb

* update time series forecast website examples

Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com>
This commit is contained in:
Kevin Chen 2022-03-08 14:21:18 -05:00 committed by GitHub
parent f0b0cae682
commit f9eda0cc40
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 3554 additions and 41 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
flaml/automl.py
View File

@ -739,7 +739,7 @@ class AutoML(BaseEstimator):
Args: Args:
X: A numpy array of featurized instances, shape n * m, X: A numpy array of featurized instances, shape n * m,
or for 'ts_forecast' task: or for ts_forecast tasks:
a pandas dataframe with the first column containing a pandas dataframe with the first column containing
timestamp values (datetime type) or an integer n for timestamp values (datetime type) or an integer n for
the predict steps (only valid when the estimator is the predict steps (only valid when the estimator is
@ -1334,13 +1334,13 @@ class AutoML(BaseEstimator):
Args: Args:
log_file_name: A string of the log file name. log_file_name: A string of the log file name.
X_train: A numpy array or dataframe of training data in shape n*m. X_train: A numpy array or dataframe of training data in shape n*m.
For 'ts_forecast' task, the first column of X_train For ts_forecast tasks, the first column of X_train
must be the timestamp column (datetime type). Other must be the timestamp column (datetime type). Other
columns in the dataframe are assumed to be exogenous columns in the dataframe are assumed to be exogenous
variables (categorical or numeric). variables (categorical or numeric).
y_train: A numpy array or series of labels in shape n*1. y_train: A numpy array or series of labels in shape n*1.
dataframe: A dataframe of training data including label column. dataframe: A dataframe of training data including label column.
For 'ts_forecast' task, dataframe must be specified and should For ts_forecast tasks, dataframe must be specified and should
have at least two columns: timestamp and label, where the first have at least two columns: timestamp and label, where the first
column is the timestamp column (datetime type). Other columns column is the timestamp column (datetime type). Other columns
in the dataframe are assumed to be exogenous variables in the dataframe are assumed to be exogenous variables
@ -1778,13 +1778,13 @@ class AutoML(BaseEstimator):
Args: Args:
X_train: A numpy array or a pandas dataframe of training data in X_train: A numpy array or a pandas dataframe of training data in
shape (n, m). For 'ts_forecast' task, the first column of X_train shape (n, m). For ts_forecast tasks, the first column of X_train
must be the timestamp column (datetime type). Other columns in must be the timestamp column (datetime type). Other columns in
the dataframe are assumed to be exogenous variables (categorical or numeric). the dataframe are assumed to be exogenous variables (categorical or numeric).
When using ray, X_train can be a ray.ObjectRef. When using ray, X_train can be a ray.ObjectRef.
y_train: A numpy array or a pandas series of labels in shape (n, ). y_train: A numpy array or a pandas series of labels in shape (n, ).
dataframe: A dataframe of training data including label column. dataframe: A dataframe of training data including label column.
For 'ts_forecast' task, dataframe must be specified and must have For ts_forecast tasks, dataframe must be specified and must have
at least two columns, timestamp and label, where the first at least two columns, timestamp and label, where the first
column is the timestamp column (datetime type). Other columns in column is the timestamp column (datetime type). Other columns in
the dataframe are assumed to be exogenous variables (categorical or numeric). the dataframe are assumed to be exogenous variables (categorical or numeric).
@ -1832,8 +1832,9 @@ class AutoML(BaseEstimator):
} }
``` ```
task: A string of the task type, e.g., task: A string of the task type, e.g.,
'classification', 'regression', 'ts_forecast', 'rank', 'classification', 'regression', 'ts_forecast_regression',
'seq-classification', 'seq-regression', 'summarization' 'ts_forecast_classification', 'rank', 'seq-classification',
'seq-regression', 'summarization'
n_jobs: An integer of the number of threads for training | default=-1. n_jobs: An integer of the number of threads for training | default=-1.
Use all available resources when n_jobs == -1. Use all available resources when n_jobs == -1.
log_file_name: A string of the log file name | default="". To disable logging, log_file_name: A string of the log file name | default="". To disable logging,
@ -1952,7 +1953,7 @@ class AutoML(BaseEstimator):
you run into OOM failures. you run into OOM failures.
**fit_kwargs: Other key word arguments to pass to fit() function of **fit_kwargs: Other key word arguments to pass to fit() function of
the searched learners, such as sample_weight. Include: the searched learners, such as sample_weight. Include:
period: int | forecast horizon for 'ts_forecast' task. period: int | forecast horizon for ts_forecast tasks.
gpu_per_trial: float, default = 0 | A float of the number of gpus per trial, gpu_per_trial: float, default = 0 | A float of the number of gpus per trial,
only used by TransformersEstimator and XGBoostSklearnEstimator. only used by TransformersEstimator and XGBoostSklearnEstimator.
""" """

1
flaml/model.py
View File

@ -1707,7 +1707,6 @@ class ARIMA(Prophet):
if len(X.columns) > 1: if len(X.columns) > 1:
X = self._preprocess(X.drop(columns=TS_TIMESTAMP_COL)) X = self._preprocess(X.drop(columns=TS_TIMESTAMP_COL))
regressors = list(X) regressors = list(X)
print(start, end, X.shape)
forecast = self._model.predict( forecast = self._model.predict(
start=start, end=end, exog=X[regressors] start=start, end=end, exog=X[regressors]
) )

3319
notebook/automl_time_series_forecast.ipynb
View File

File diff suppressed because one or more lines are too long

1
test/automl/test_forecast.py
View File

@ -371,7 +371,6 @@ def test_multivariate_forecast_cat(budget=5):
def test_forecast_classification(budget=5): def test_forecast_classification(budget=5):
from hcrystalball.utils import get_sales_data from hcrystalball.utils import get_sales_data
from hcrystalball.wrappers import get_sklearn_wrapper
time_horizon = 30 time_horizon = 30
df = get_sales_data(n_dates=180, n_assortments=1, n_states=1, n_stores=1) df = get_sales_data(n_dates=180, n_assortments=1, n_states=1, n_stores=1)

257
website/docs/Examples/AutoML-Time series forecast.md
View File

@ -7,7 +7,7 @@ Install the [ts_forecast] option.
pip install "flaml[ts_forecast]" pip install "flaml[ts_forecast]"
``` ```
### Univariate time series ### Simple NumPy Example
```python ```python
import numpy as np import numpy as np
@ -239,7 +239,7 @@ print(automl.predict(X_train[84:]))
[flaml.automl: 01-21 08:01:28] {2594} INFO - at 7.3s, estimator xgb_limitdepth's best error=0.9683, best estimator sarimax's best error=0.5600 [flaml.automl: 01-21 08:01:28] {2594} INFO - at 7.3s, estimator xgb_limitdepth's best error=0.9683, best estimator sarimax's best error=0.5600
``` ```
### Multivariate time series ### Univariate time series
```python ```python
import statsmodels.api as sm import statsmodels.api as sm
@ -426,4 +426,257 @@ plt.legend()
![png](images/CO2.png) ![png](images/CO2.png)
### Multivariate Time Series (Forecasting with Exogeneous Variables)
```python
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
```python
[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
```python
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
```python
[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](https://github.com/microsoft/FLAML/blob/main/notebook/automl_time_series_forecast.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_time_series_forecast.ipynb) [Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/automl_time_series_forecast.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_time_series_forecast.ipynb)