mirror of https://github.com/microsoft/autogen.git
update documentation for time series forecasting (#472)
* 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>
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Kevin Chen
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@ -739,7 +739,7 @@ class AutoML(BaseEstimator):
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Args:
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X: A numpy array of featurized instances, shape n * m,
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or for 'ts_forecast' task:
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or for ts_forecast tasks:
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a pandas dataframe with the first column containing
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timestamp values (datetime type) or an integer n for
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the predict steps (only valid when the estimator is
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@ -1334,13 +1334,13 @@ class AutoML(BaseEstimator):
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Args:
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log_file_name: A string of the log file name.
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X_train: A numpy array or dataframe of training data in shape n*m.
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For 'ts_forecast' task, the first column of X_train
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For ts_forecast tasks, the first column of X_train
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must be the timestamp column (datetime type). Other
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columns in the dataframe are assumed to be exogenous
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variables (categorical or numeric).
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y_train: A numpy array or series of labels in shape n*1.
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dataframe: A dataframe of training data including label column.
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For 'ts_forecast' task, dataframe must be specified and should
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For ts_forecast tasks, dataframe must be specified and should
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have at least two columns: timestamp and label, where the first
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column is the timestamp column (datetime type). Other columns
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in the dataframe are assumed to be exogenous variables
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@ -1778,13 +1778,13 @@ class AutoML(BaseEstimator):
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Args:
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X_train: A numpy array or a pandas dataframe of training data in
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shape (n, m). For 'ts_forecast' task, the first column of X_train
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shape (n, m). For ts_forecast tasks, the first column of X_train
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must be the timestamp column (datetime type). Other columns in
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the dataframe are assumed to be exogenous variables (categorical or numeric).
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When using ray, X_train can be a ray.ObjectRef.
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y_train: A numpy array or a pandas series of labels in shape (n, ).
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dataframe: A dataframe of training data including label column.
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For 'ts_forecast' task, dataframe must be specified and must have
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For ts_forecast tasks, dataframe must be specified and must have
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at least two columns, timestamp and label, where the first
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column is the timestamp column (datetime type). Other columns in
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the dataframe are assumed to be exogenous variables (categorical or numeric).
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@ -1832,8 +1832,9 @@ class AutoML(BaseEstimator):
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}
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```
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task: A string of the task type, e.g.,
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'classification', 'regression', 'ts_forecast', 'rank',
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'seq-classification', 'seq-regression', 'summarization'
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'classification', 'regression', 'ts_forecast_regression',
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'ts_forecast_classification', 'rank', 'seq-classification',
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'seq-regression', 'summarization'
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n_jobs: An integer of the number of threads for training | default=-1.
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Use all available resources when n_jobs == -1.
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log_file_name: A string of the log file name | default="". To disable logging,
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@ -1952,7 +1953,7 @@ class AutoML(BaseEstimator):
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you run into OOM failures.
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**fit_kwargs: Other key word arguments to pass to fit() function of
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the searched learners, such as sample_weight. Include:
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period: int | forecast horizon for 'ts_forecast' task.
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period: int | forecast horizon for ts_forecast tasks.
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gpu_per_trial: float, default = 0 | A float of the number of gpus per trial,
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only used by TransformersEstimator and XGBoostSklearnEstimator.
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"""
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@ -1707,7 +1707,6 @@ class ARIMA(Prophet):
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if len(X.columns) > 1:
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X = self._preprocess(X.drop(columns=TS_TIMESTAMP_COL))
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regressors = list(X)
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print(start, end, X.shape)
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forecast = self._model.predict(
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start=start, end=end, exog=X[regressors]
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)
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File diff suppressed because one or more lines are too long
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@ -371,7 +371,6 @@ def test_multivariate_forecast_cat(budget=5):
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def test_forecast_classification(budget=5):
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from hcrystalball.utils import get_sales_data
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from hcrystalball.wrappers import get_sklearn_wrapper
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time_horizon = 30
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df = get_sales_data(n_dates=180, n_assortments=1, n_states=1, n_stores=1)
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@ -7,7 +7,7 @@ Install the [ts_forecast] option.
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pip install "flaml[ts_forecast]"
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```
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### Univariate time series
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### Simple NumPy Example
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```python
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import numpy as np
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@ -239,7 +239,7 @@ print(automl.predict(X_train[84:]))
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[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
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```
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### Multivariate time series
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### Univariate time series
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```python
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import statsmodels.api as sm
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@ -426,4 +426,257 @@ plt.legend()
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### Multivariate Time Series (Forecasting with Exogeneous Variables)
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```python
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import pandas as pd
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# pd.set_option("display.max_rows", None, "display.max_columns", None)
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multi_df = pd.read_csv(
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"https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/nyc_energy_consumption.csv"
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)
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# preprocessing data
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multi_df["timeStamp"] = pd.to_datetime(multi_df["timeStamp"])
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multi_df = multi_df.set_index("timeStamp")
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multi_df = multi_df.resample("D").mean()
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multi_df["temp"] = multi_df["temp"].fillna(method="ffill")
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multi_df["precip"] = multi_df["precip"].fillna(method="ffill")
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multi_df = multi_df[:-2] # last two rows are NaN for 'demand' column so remove them
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multi_df = multi_df.reset_index()
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# Using temperature values create categorical values
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# where 1 denotes daily tempurature is above monthly average and 0 is below.
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def get_monthly_avg(data):
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data["month"] = data["timeStamp"].dt.month
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data = data[["month", "temp"]].groupby("month")
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data = data.agg({"temp": "mean"})
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return data
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monthly_avg = get_monthly_avg(multi_df).to_dict().get("temp")
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def above_monthly_avg(date, temp):
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month = date.month
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if temp > monthly_avg.get(month):
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return 1
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else:
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return 0
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multi_df["temp_above_monthly_avg"] = multi_df.apply(
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lambda x: above_monthly_avg(x["timeStamp"], x["temp"]), axis=1
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)
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del multi_df["temp"], multi_df["month"] # remove temperature column to reduce redundancy
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# split data into train and test
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num_samples = multi_df.shape[0]
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multi_time_horizon = 180
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split_idx = num_samples - multi_time_horizon
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multi_train_df = multi_df[:split_idx]
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multi_test_df = multi_df[split_idx:]
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multi_X_test = multi_test_df[
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["timeStamp", "precip", "temp_above_monthly_avg"]
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] # test dataframe must contain values for the regressors / multivariate variables
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multi_y_test = multi_test_df["demand"]
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# initialize AutoML instance
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automl = AutoML()
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# configure AutoML settings
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settings = {
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"time_budget": 10, # total running time in seconds
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"metric": "mape", # primary metric
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"task": "ts_forecast", # task type
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"log_file_name": "energy_forecast_categorical.log", # flaml log file
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"eval_method": "holdout",
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"log_type": "all",
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"label": "demand",
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}
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# train the model
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automl.fit(dataframe=df, **settings, period=time_horizon)
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# predictions
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print(automl.predict(multi_X_test))
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```
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#### Sample Output
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```python
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 15, current learner xgboost
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 16, current learner extra_tree
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 17, current learner extra_tree
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 18, current learner xgboost
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 19, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 20, current learner xgboost
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 21, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 22, current learner lgbm
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 23, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 24, current learner extra_tree
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 25, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 26, current learner rf
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 27, current learner rf
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[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
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[flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 28, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:32:27] {2458} INFO - iteration 29, current learner sarimax
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[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
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[flaml.automl: 02-28 21:32:28] {2458} INFO - iteration 30, current learner xgboost
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[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
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[flaml.automl: 02-28 21:32:28] {2458} INFO - iteration 31, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:32:28] {2458} INFO - iteration 32, current learner arima
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[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
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[flaml.automl: 02-28 21:32:32] {2850} INFO - retrain prophet for 2.2s
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[flaml.automl: 02-28 21:32:32] {2857} INFO - retrained model: <prophet.forecaster.Prophet object at 0x000001B1D3EE2B80>
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[flaml.automl: 02-28 21:32:32] {2234} INFO - fit succeeded
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[flaml.automl: 02-28 21:32:32] {2235} INFO - Time taken to find the best model: 4.351356506347656
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```
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### Forecasting Discrete Variables
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```python
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from hcrystalball.utils import get_sales_data
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import numpy as np
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from flaml import AutoML
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time_horizon = 30
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df = get_sales_data(n_dates=180, n_assortments=1, n_states=1, n_stores=1)
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df = df[["Sales", "Open", "Promo", "Promo2"]]
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# feature engineering - create a discrete value column
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# 1 denotes above mean and 0 denotes below mean
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df["above_mean_sales"] = np.where(df["Sales"] > df["Sales"].mean(), 1, 0)
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df.reset_index(inplace=True)
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# train-test split
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discrete_train_df = df[:-time_horizon]
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discrete_test_df = df[-time_horizon:]
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discrete_X_train, discrete_X_test = (
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discrete_train_df[["Date", "Open", "Promo", "Promo2"]],
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discrete_test_df[["Date", "Open", "Promo", "Promo2"]],
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)
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discrete_y_train, discrete_y_test = discrete_train_df["above_mean_sales"], discrete_test_df["above_mean_sales"]
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# initialize AutoML instance
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automl = AutoML()
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# configure the settings
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settings = {
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"time_budget": 15, # total running time in seconds
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"metric": "accuracy", # primary metric
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"task": "ts_forecast_classification", # task type
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"log_file_name": "sales_classification_forecast.log", # flaml log file
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"eval_method": "holdout",
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}
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# train the model
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automl.fit(X_train=discrete_X_train,
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y_train=discrete_y_train,
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**settings,
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period=time_horizon)
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# make predictions
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discrete_y_pred = automl.predict(discrete_X_test)
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print("Predicted label", discrete_y_pred)
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print("True label", discrete_y_test)
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```
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#### Sample Output
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```python
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[flaml.automl: 02-28 21:53:03] {2060} INFO - task = ts_forecast_classification
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[flaml.automl: 02-28 21:53:03] {2062} INFO - Data split method: time
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[flaml.automl: 02-28 21:53:03] {2066} INFO - Evaluation method: holdout
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[flaml.automl: 02-28 21:53:03] {2147} INFO - Minimizing error metric: 1-accuracy
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[flaml.automl: 02-28 21:53:03] {2205} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth']
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 0, current learner lgbm
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[flaml.automl: 02-28 21:53:03] {2573} INFO - Estimated sufficient time budget=269s. Estimated necessary time budget=0s.
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 1, current learner lgbm
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 2, current learner lgbm
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 3, current learner rf
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 4, current learner xgboost
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 5, current learner lgbm
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 6, current learner rf
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 7, current learner lgbm
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 8, current learner lgbm
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 9, current learner lgbm
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 10, current learner rf
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 11, current learner rf
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 12, current learner xgboost
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 13, current learner extra_tree
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 14, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 15, current learner xgboost
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 16, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 17, current learner rf
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 18, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 19, current learner lgbm
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 20, current learner extra_tree
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 21, current learner xgboost
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 22, current learner extra_tree
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[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
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[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 23, current learner rf
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[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
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[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 24, current learner xgboost
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[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
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[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 25, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 26, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 27, current learner xgboost
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[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
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[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 28, current learner extra_tree
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[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
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[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 29, current learner xgb_limitdepth
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[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
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[flaml.automl: 02-28 21:53:04] {2850} INFO - retrain xgboost for 0.0s
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[flaml.automl: 02-28 21:53:04] {2857} INFO - retrained model: XGBClassifier(base_score=0.5, booster='gbtree',
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colsample_bylevel=0.9826753651836615, colsample_bynode=1,
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colsample_bytree=0.9725493834064914, gamma=0, gpu_id=-1,
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grow_policy='lossguide', importance_type='gain',
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interaction_constraints='', learning_rate=0.1665803484560213,
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max_delta_step=0, max_depth=0, max_leaves=4,
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min_child_weight=0.5649012460525115, missing=nan,
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monotone_constraints='()', n_estimators=4, n_jobs=-1,
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num_parallel_tree=1, objective='binary:logistic', random_state=0,
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reg_alpha=0.009638363373006869, reg_lambda=0.143703802530408,
|
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scale_pos_weight=1, subsample=0.9643606787051899,
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tree_method='hist', use_label_encoder=False,
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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)
|
||||
Loading…
Reference in New Issue