mirror of https://github.com/microsoft/autogen.git
136 lines
3.3 KiB
Python
136 lines
3.3 KiB
Python
from sklearn.datasets import make_classification
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import numpy as np
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from pandas import DataFrame
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from datetime import datetime
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from flaml.model import (
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KNeighborsEstimator,
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LRL2Classifier,
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BaseEstimator,
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LGBMEstimator,
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CatBoostEstimator,
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XGBoostEstimator,
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RandomForestEstimator,
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Prophet,
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ARIMA,
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LGBM_TS,
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)
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def test_lrl2():
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BaseEstimator.search_space(1, "")
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X, y = make_classification(100000, 1000)
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print("start")
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lr = LRL2Classifier()
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lr.predict(X)
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lr.fit(X, y, budget=1e-5)
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def test_prep():
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X = np.array(
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list(
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zip(
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[
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3.0,
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16.0,
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10.0,
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12.0,
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3.0,
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14.0,
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11.0,
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12.0,
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5.0,
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14.0,
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20.0,
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16.0,
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15.0,
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11.0,
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],
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[
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"a",
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"b",
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"a",
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"c",
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"c",
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"b",
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"b",
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"b",
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"b",
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"a",
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"b",
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1.0,
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1.0,
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"a",
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],
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)
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),
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dtype=object,
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)
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y = np.array([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
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lr = LRL2Classifier()
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lr.fit(X, y)
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lr.predict(X)
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print(lr.feature_names_in_)
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print(lr.feature_importances_)
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lgbm = LGBMEstimator(n_estimators=4)
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lgbm.fit(X, y)
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print(lgbm.feature_names_in_)
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print(lgbm.feature_importances_)
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cat = CatBoostEstimator(n_estimators=4)
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cat.fit(X, y)
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print(cat.feature_names_in_)
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print(cat.feature_importances_)
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knn = KNeighborsEstimator(task="regression")
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knn.fit(X, y)
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print(knn.feature_names_in_)
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print(knn.feature_importances_)
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xgb = XGBoostEstimator(n_estimators=4, max_leaves=4)
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xgb.fit(X, y)
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xgb.predict(X)
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print(xgb.feature_names_in_)
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print(xgb.feature_importances_)
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rf = RandomForestEstimator(task="regression", n_estimators=4, criterion="gini")
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rf.fit(X, y)
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print(rf.feature_names_in_)
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print(rf.feature_importances_)
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prophet = Prophet()
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try:
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prophet.predict(4)
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except ValueError:
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# predict() with steps is only supported for arima/sarimax.
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pass
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prophet.predict(X)
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arima = ARIMA()
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arima.predict(X)
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arima._model = False
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try:
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arima.predict(X)
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except ValueError:
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# X_test needs to be either a pandas Dataframe with dates as the first column or an int number of periods for predict().
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pass
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lgbm = LGBM_TS(optimize_for_horizon=True, lags=1)
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X = DataFrame(
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{
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"A": [
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datetime(1900, 2, 3),
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datetime(1900, 3, 4),
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datetime(1900, 3, 4),
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datetime(1900, 3, 4),
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datetime(1900, 7, 2),
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datetime(1900, 8, 9),
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],
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}
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)
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y = np.array([0, 1, 0, 1, 0, 0])
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lgbm.predict(X[:2])
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lgbm.fit(X, y, period=2)
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lgbm.predict(X[:2])
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print(lgbm.feature_names_in_)
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print(lgbm.feature_importances_)
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if __name__ == "__main__":
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test_prep()
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