fromfeature_engine.selectionimportDropFeatures fromfeature_engine.timeseries.forecastingimportLagFeatures, WindowFeatures fromsklearn.pipelineimportPipeline 我们还将从Sklearn导入Pipeline,它可以帮助我们执行特征工程,然后载入数据,排序,然后做简单的数据清理 defload_data(): # Data lives here. filename="AirQuality...
time_series["value2"] = time_series["value"] * 0.5 + np.random.normal(0, 0.05, len(time_series)) # 执行多变量特征提取 features_multivariate = extract_features( time_series, column_id="id", column_sort="time", default_fc_parameters=EfficientFCParameters(), n_jobs=0 ) print("\nMult...
data.head())# print('Values:\n', data)# === Step 2.1: Normalize Data (0-1) ===#data, normalize_modele = normalize_regression(data, type_normalize='MinMaxScaler', display_figure='on') # Type_Normalize: 'MinMaxScaler', 'nor...
importances=[]# Iterate over all columns and remove one at a timeforiinrange(X_train.shape[1]):X_temp=np.delete(X_train,i,axis=1)rf.fit(X_temp,y_train)acc=accuracy_score(y_test,rf.predict(np.delete(X_test,i,axis=1)))importances.append(base_acc-acc)# Plot importance scores ...
plt.xlabel('Time') plt.ylabel('Value') plt.legend() plt.savefig("sample_TS.png") plt.show() 数据展现出预期的随机性特征,这与实际时间序列数据的特性相符。 特征提取过程 数据呈现出典型的时间序列特征,包含噪声和波动。下面使用 tsfresh.extract_features ...
# Iterate over all columns and remove one at a time foriinrange(X_train.shape[1]): X_temp = np.delete(X_train, i, axis=1) rf.fit(X_temp, y_train) acc = accuracy_score(y_test, rf.predict(np.delete(X_test, i, axis=1))) ...
If you're a Python developer who relies on the Eventlet library, it's time to think about migrating your projects to Asyncio. This article helps you get started. Article GPU benchmarking and how to choose a GPU framework Kenny Ge
def plotMovingAverage(series, window, plot_intervals=False, scale=1.96, plot_anomalies=False): """ series - dataframe with timeseries window - rolling window size plot_intervals - show confidence intervals plot_anomalies - show anomalies """ rolling_mean = series.rolling(window=window).mean()...
labels, ax): ax.plot(date_time, data) ax.set_xlim(date2num(np.min(date_time)),...
batch = next(iter(train_dataloader)) 可以看出,我们没有将 input_ids 和 attention_mask 提供给编码器 (训练 NLP 模型时也是这种情况),而是提供 past_values,以及 past_observed_mask、past_time_features、static_categorical_features 和 static_real_features 几项数据。 解码器的输入包括 future_values、future...